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zhang qiao
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•
9ddee9f
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Parent(s):
5e655a5
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- .gitignore +163 -0
- README.md +13 -8
- __init__.py +0 -0
- __pycache__/demo.cpython-310.pyc +0 -0
- best_models.csv +11 -0
- conda_installs.txt +13 -0
- data/continuous.csv +33 -0
- data/demand_forecasting_demo_data.csv +2573 -0
- data/demand_forecasting_demo_models.csv +11 -0
- data/fuzzy.csv +261 -0
- data/fuzzy_2.csv +37 -0
- data/resource.md +1 -0
- demo.py +121 -0
- environment.yml +222 -0
- forecast_result.csv +521 -0
- gr_app/GradioApp.py +162 -0
- gr_app/__init__.py +1 -0
- gr_app/__pycache__/GradioApp.cpython-310.pyc +0 -0
- gr_app/__pycache__/__init__.cpython-310.pyc +0 -0
- gr_app/__pycache__/args.cpython-310.pyc +0 -0
- gr_app/args.py +16 -0
- model.csv +11 -0
- notebooks/res.txt +0 -0
- notebooks/test.ipynb +828 -0
- src/__init__.py +0 -0
- src/__pycache__/__init__.cpython-310.pyc +0 -0
- src/__pycache__/__init__.cpython-311.pyc +0 -0
- src/__pycache__/avtive_models.cpython-310.pyc +0 -0
- src/__pycache__/main.cpython-310.pyc +0 -0
- src/__pycache__/main.cpython-311.pyc +0 -0
- src/avtive_models.py +19 -0
- src/forecast/Prophet.py +22 -0
- src/forecast/__init__.py +0 -0
- src/forecast/__pycache__/Prophet.cpython-310.pyc +0 -0
- src/forecast/__pycache__/Prophet.cpython-311.pyc +0 -0
- src/forecast/__pycache__/__init__.cpython-310.pyc +0 -0
- src/forecast/__pycache__/__init__.cpython-311.pyc +0 -0
- src/functions/__init__.py +0 -0
- src/functions/__pycache__/__init__.cpython-310.pyc +0 -0
- src/functions/__pycache__/__init__.cpython-311.pyc +0 -0
- src/functions/__pycache__/check_input.cpython-310.pyc +0 -0
- src/functions/__pycache__/check_input.cpython-311.pyc +0 -0
- src/functions/__pycache__/itmtt_scores.cpython-310.pyc +0 -0
- src/functions/__pycache__/mase.cpython-310.pyc +0 -0
- src/functions/__pycache__/order_qty_rmse.cpython-310.pyc +0 -0
- src/functions/check_input.py +5 -0
- src/functions/itmtt_scores.py +29 -0
- src/functions/mase.py +13 -0
- src/functions/order_qty_rmse.py +13 -0
- src/functions/sort_res.py +6 -0
.gitignore
ADDED
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# Byte-compiled / optimized / DLL files
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2 |
+
__pycache__/
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+
*.py[cod]
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*$py.class
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# C extensions
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7 |
+
*.so
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+
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+
# Distribution / packaging
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10 |
+
.Python
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11 |
+
build/
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12 |
+
develop-eggs/
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13 |
+
dist/
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downloads/
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15 |
+
eggs/
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.eggs/
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lib/
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+
lib64/
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+
parts/
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+
sdist/
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21 |
+
var/
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22 |
+
wheels/
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+
share/python-wheels/
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+
*.egg-info/
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+
.installed.cfg
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26 |
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*.egg
|
27 |
+
MANIFEST
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# PyInstaller
|
30 |
+
# Usually these files are written by a python script from a template
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31 |
+
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
32 |
+
*.manifest
|
33 |
+
*.spec
|
34 |
+
|
35 |
+
# Installer logs
|
36 |
+
pip-log.txt
|
37 |
+
pip-delete-this-directory.txt
|
38 |
+
|
39 |
+
# Unit test / coverage reports
|
40 |
+
htmlcov/
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41 |
+
.tox/
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42 |
+
.nox/
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43 |
+
.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
|
55 |
+
*.mo
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56 |
+
*.pot
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57 |
+
|
58 |
+
# Django stuff:
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59 |
+
*.log
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60 |
+
local_settings.py
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61 |
+
db.sqlite3
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62 |
+
db.sqlite3-journal
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63 |
+
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64 |
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# Flask stuff:
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65 |
+
instance/
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66 |
+
.webassets-cache
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67 |
+
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68 |
+
# Scrapy stuff:
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69 |
+
.scrapy
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70 |
+
|
71 |
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# Sphinx documentation
|
72 |
+
docs/_build/
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73 |
+
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# PyBuilder
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75 |
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.pybuilder/
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target/
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+
|
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# Jupyter Notebook
|
79 |
+
.ipynb_checkpoints
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80 |
+
|
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# IPython
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+
profile_default/
|
83 |
+
ipython_config.py
|
84 |
+
|
85 |
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# pyenv
|
86 |
+
# For a library or package, you might want to ignore these files since the code is
|
87 |
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# intended to run in multiple environments; otherwise, check them in:
|
88 |
+
# .python-version
|
89 |
+
|
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+
# pipenv
|
91 |
+
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
92 |
+
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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93 |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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|
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# poetry
|
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
|
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|
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# pdm
|
105 |
+
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
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#pdm.lock
|
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+
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
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__pypackages__/
|
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|
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# Celery stuff
|
116 |
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celerybeat-schedule
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117 |
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celerybeat.pid
|
118 |
+
|
119 |
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# SageMath parsed files
|
120 |
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*.sage.py
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|
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# Environments
|
123 |
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
|
132 |
+
.spyderproject
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.spyproject
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+
|
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# Rope project settings
|
136 |
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.ropeproject
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|
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# mkdocs documentation
|
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
|
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dmypy.json
|
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|
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# Pyre type checker
|
147 |
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.pyre/
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|
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# pytype static type analyzer
|
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.pytype/
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|
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# Cython debug symbols
|
153 |
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cython_debug/
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|
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# PyCharm
|
156 |
+
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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157 |
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
158 |
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# and can be added to the global gitignore or merged into this file. For a more nuclear
|
159 |
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
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#.idea/
|
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|
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.DS_Store
|
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*/.DS_Store
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README.md
CHANGED
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---
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-
title:
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colorFrom: red
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colorTo: green
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sdk: gradio
|
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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---
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title: demand-forecasting
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app_file: demo.py
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sdk: gradio
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sdk_version: 3.41.0
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---
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### Update conda environment
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```sh
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conda env update --file environment.yml --prune
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```
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### Add conda environment to ipykernel
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```sh
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python -m ipykernel install --user --name demand-forecasting
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```
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### to run gradio app
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__init__.py
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File without changes
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__pycache__/demo.cpython-310.pyc
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Binary file (3.47 kB). View file
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best_models.csv
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sku,best_model,characteristic,RMSE
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sku-0,fft_plus,continuous,20.29778313018444
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sku-1,holt_winters_plus,continuous,48.49842843820416
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sku-2,prophet_plus,fuzzy,39.28846310729568
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sku-3,prophet_plus,fuzzy_transient,14.593201789242087
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sku-4,prophet_plus,fuzzy,10.7747925198657
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sku-5,prophet_plus,fuzzy,28.33012802382216
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sku-6,ceif_plus,fuzzy,37.84242038358283
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sku-7,holt_winters_plus,continuous,15.959770854065722
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sku-8,prophet_plus,fuzzy,13.778467035419936
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sku-9,prophet_plus,fuzzy,12.843706019437128
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conda_installs.txt
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conda install -c anaconda ipykernel -y
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conda install -c anaconda urllib3 -y
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conda install -c conda-forge gradio -y
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conda install -c conda-forge prophet -y
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conda install -c anaconda pandas -y
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conda install scikit-learn -y
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conda install -c intel pyyaml -y
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conda install -c conda-forge python-dotenv -y
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(if conda version of gradio doesn't work)
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pip install gradio
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data/continuous.csv
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datetime,y
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2020-05-31,150.0
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2020-06-30,508.0
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2020-07-31,292.0
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2020-08-31,800.0
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2020-09-30,800.0
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2020-10-31,800.0
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2020-11-30,300.0
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2020-12-31,300.0
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2021-01-31,237.0
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2021-02-28,237.0
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2021-03-31,600.0
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2021-04-30,200.0
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2021-05-31,600.0
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2021-06-30,400.0
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2021-07-31,1300.0
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2021-08-31,2000.0
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2021-09-30,6500.0
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2021-10-31,1100.0
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2021-11-30,1000.0
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2021-12-31,2000.0
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2022-01-31,3000.0
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2022-02-28,2200.0
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2022-03-31,6800.0
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2022-04-30,2000.0
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2022-05-31,6000.0
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2022-06-30,5300.0
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2022-07-31,3000.0
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2022-08-31,2900.0
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2022-09-30,13600.0
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2022-10-31,15400.0
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2022-11-30,14800.0
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2022-12-31,4000.0
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data/demand_forecasting_demo_data.csv
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1 |
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datetime,y,sku
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1869 |
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1870 |
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1872 |
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1877 |
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1878 |
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1879 |
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1880 |
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1881 |
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1882 |
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1883 |
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1884 |
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2019-12-22,12,sku-7
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1885 |
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2019-12-29,32,sku-7
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1886 |
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1887 |
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1888 |
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1889 |
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1890 |
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1891 |
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1892 |
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1893 |
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1894 |
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1895 |
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1896 |
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1897 |
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1898 |
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1899 |
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1900 |
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1901 |
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1902 |
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2020-04-26,4,sku-7
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1903 |
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1904 |
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1905 |
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2020-05-17,22,sku-7
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1906 |
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1907 |
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1908 |
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2020-06-07,12,sku-7
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1909 |
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2020-06-14,5,sku-7
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1910 |
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2020-06-21,11,sku-7
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1911 |
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2020-06-28,12,sku-7
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1912 |
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2020-07-05,5,sku-7
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1913 |
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2020-07-12,10,sku-7
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1914 |
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2020-07-19,12,sku-7
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1915 |
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2020-07-26,42,sku-7
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1916 |
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2020-08-02,12,sku-7
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1917 |
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2020-08-09,12,sku-7
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1918 |
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2020-08-16,12,sku-7
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1919 |
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2020-08-23,140,sku-7
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1920 |
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2020-08-30,55,sku-7
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1921 |
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2020-09-06,12,sku-7
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1922 |
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2020-09-13,12,sku-7
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1923 |
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2020-09-20,12,sku-7
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1924 |
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2020-09-27,12,sku-7
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1925 |
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2020-10-04,12,sku-7
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1926 |
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2020-10-11,12,sku-7
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1927 |
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2020-10-18,12,sku-7
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1928 |
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2020-10-25,12,sku-7
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1929 |
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2020-11-01,12,sku-7
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1930 |
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2020-11-08,12,sku-7
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1931 |
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2020-11-15,12,sku-7
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1932 |
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2020-11-22,12,sku-7
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1933 |
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2020-11-29,12,sku-7
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1934 |
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2020-12-06,9,sku-7
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1935 |
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2020-12-13,5,sku-7
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1936 |
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2020-12-20,12,sku-7
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1937 |
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2020-12-27,10,sku-7
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1938 |
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2021-01-03,12,sku-7
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1939 |
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2021-01-10,12,sku-7
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1940 |
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2021-01-17,12,sku-7
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1941 |
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2021-01-24,6,sku-7
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1942 |
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2021-01-31,24,sku-7
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1943 |
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2021-02-07,10,sku-7
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1944 |
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2021-02-14,82,sku-7
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1945 |
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2021-02-21,12,sku-7
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1946 |
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2021-02-28,12,sku-7
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1947 |
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2021-03-07,12,sku-7
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1948 |
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2021-03-14,12,sku-7
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1949 |
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2021-03-21,35,sku-7
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1950 |
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2021-03-28,12,sku-7
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1951 |
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2021-04-04,12,sku-7
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1952 |
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2021-04-11,12,sku-7
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1953 |
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2021-04-18,12,sku-7
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1954 |
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2021-04-25,18,sku-7
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1955 |
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2021-05-02,10,sku-7
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1956 |
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2021-05-09,12,sku-7
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1957 |
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2021-05-16,12,sku-7
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1958 |
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2021-05-23,3,sku-7
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1959 |
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2021-05-30,3,sku-7
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1960 |
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2021-06-06,5,sku-7
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1961 |
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2021-06-13,1,sku-7
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1962 |
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2021-06-20,2,sku-7
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1963 |
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2021-06-27,15,sku-7
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1964 |
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2021-07-04,15,sku-7
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1965 |
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2021-07-11,2,sku-7
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1966 |
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2021-07-18,2,sku-7
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1967 |
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2021-07-25,2,sku-7
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1968 |
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2021-08-01,10,sku-7
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1969 |
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2021-08-08,20,sku-7
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1970 |
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2021-08-15,25,sku-7
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1971 |
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2021-08-22,12,sku-7
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1972 |
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2021-08-29,12,sku-7
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1973 |
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2021-09-05,12,sku-7
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1974 |
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2021-09-12,12,sku-7
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1975 |
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2021-09-19,12,sku-7
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1976 |
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2021-09-26,5,sku-7
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1977 |
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2021-10-03,12,sku-7
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1978 |
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2021-10-10,7,sku-7
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1979 |
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2021-10-17,25,sku-7
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1980 |
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2021-10-24,10,sku-7
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1981 |
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2021-10-31,5,sku-7
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1982 |
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2021-11-07,15,sku-7
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1983 |
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2021-11-14,8,sku-7
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1984 |
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2021-11-21,14,sku-7
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1985 |
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2021-11-28,18,sku-7
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1986 |
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2021-12-05,65,sku-7
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1987 |
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2021-12-12,12,sku-7
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1988 |
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2021-12-19,12,sku-7
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1989 |
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2021-12-26,12,sku-7
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1990 |
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2022-01-02,10,sku-7
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1991 |
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2022-01-09,15,sku-7
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1992 |
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2022-01-16,1,sku-7
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1993 |
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2022-01-23,60,sku-7
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1994 |
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2022-01-30,12,sku-7
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1995 |
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1996 |
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2022-02-13,20,sku-7
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1997 |
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2022-02-20,10,sku-7
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1998 |
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2022-02-27,12,sku-7
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1999 |
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2000 |
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2022-03-13,12,sku-7
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2001 |
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2022-03-20,12,sku-7
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2002 |
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2022-03-27,20,sku-7
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2003 |
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2022-04-03,12,sku-7
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2004 |
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2022-04-10,25,sku-7
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2005 |
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2022-04-17,15,sku-7
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2006 |
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2022-04-24,15,sku-7
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2007 |
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2022-05-01,10,sku-7
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2008 |
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2022-05-08,12,sku-7
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2009 |
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2022-05-15,13,sku-7
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2010 |
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2022-05-22,7,sku-7
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2011 |
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2022-05-29,26,sku-7
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2012 |
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2022-06-05,18,sku-7
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2013 |
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2022-06-12,12,sku-7
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2014 |
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2022-06-19,12,sku-7
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2015 |
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2022-06-26,12,sku-7
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2016 |
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2022-07-03,35,sku-7
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2017 |
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2022-07-10,20,sku-7
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2018 |
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2022-07-17,30,sku-7
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2019 |
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2022-07-24,8,sku-7
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2020 |
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2022-07-31,12,sku-7
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2021 |
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2022-08-07,50,sku-7
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2022 |
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2022-08-14,33,sku-7
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2023 |
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2022-08-21,12,sku-7
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2024 |
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2022-08-28,12,sku-7
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2025 |
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2022-09-04,10,sku-7
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2026 |
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2022-09-11,10,sku-7
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2027 |
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2022-09-18,12,sku-7
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2028 |
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2022-09-25,20,sku-7
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2029 |
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2022-10-02,20,sku-7
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2030 |
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2022-10-09,12,sku-7
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2031 |
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2022-10-16,50,sku-7
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2032 |
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2022-10-23,12,sku-7
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2033 |
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2022-10-30,12,sku-7
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2034 |
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2022-11-06,12,sku-7
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2035 |
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2022-11-13,12,sku-7
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2036 |
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2022-11-20,12,sku-7
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2037 |
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2022-11-27,20,sku-7
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2038 |
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2022-12-04,50,sku-7
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2039 |
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2022-12-11,60,sku-7
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2040 |
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2022-12-18,10,sku-7
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2041 |
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2365 |
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2366 |
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2367 |
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2369 |
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2398 |
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2399 |
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2400 |
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2401 |
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2402 |
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2403 |
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2407 |
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2408 |
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2409 |
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2410 |
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2411 |
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2412 |
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2413 |
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2414 |
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2415 |
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2416 |
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2417 |
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2418 |
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2419 |
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2421 |
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2422 |
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2423 |
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2450 |
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2021-03-21,10,sku-9
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2021-03-28,20,sku-9
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2021-05-02,17,sku-9
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2021-06-27,32,sku-9
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2489 |
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2021-09-12,20,sku-9
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2490 |
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2021-09-19,0,sku-9
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2491 |
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2021-09-26,0,sku-9
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2492 |
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2021-10-03,0,sku-9
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2493 |
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2021-10-10,0,sku-9
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2494 |
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2021-10-17,5,sku-9
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2495 |
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2021-10-24,15,sku-9
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2496 |
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2021-10-31,10,sku-9
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2497 |
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2021-11-07,0,sku-9
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2498 |
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2021-11-14,5,sku-9
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2499 |
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2021-11-21,11,sku-9
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2500 |
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2021-11-28,25,sku-9
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2501 |
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2021-12-05,48,sku-9
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2502 |
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2021-12-12,7,sku-9
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2503 |
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2021-12-19,0,sku-9
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2504 |
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2021-12-26,5,sku-9
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2505 |
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2022-01-02,10,sku-9
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2506 |
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2022-01-09,0,sku-9
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2507 |
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2022-01-16,7,sku-9
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2508 |
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2022-01-23,15,sku-9
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2509 |
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2022-01-30,0,sku-9
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2510 |
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2022-02-06,5,sku-9
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2511 |
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2022-02-13,30,sku-9
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2512 |
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2022-02-20,20,sku-9
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2513 |
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2022-02-27,0,sku-9
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2514 |
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2022-03-06,25,sku-9
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2515 |
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2022-03-13,15,sku-9
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2516 |
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2022-03-20,15,sku-9
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2517 |
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2022-03-27,15,sku-9
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2518 |
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2022-04-03,10,sku-9
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2519 |
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2022-04-10,30,sku-9
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2520 |
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2022-04-17,0,sku-9
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2521 |
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2022-04-24,0,sku-9
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2522 |
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2022-05-01,0,sku-9
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2523 |
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2022-05-08,0,sku-9
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2524 |
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2022-05-15,20,sku-9
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2525 |
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2022-05-22,0,sku-9
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2526 |
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2022-05-29,0,sku-9
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2527 |
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2022-06-05,10,sku-9
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2528 |
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2529 |
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2530 |
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2022-06-26,20,sku-9
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2531 |
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2022-07-03,30,sku-9
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2532 |
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2022-07-10,0,sku-9
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2022-07-17,0,sku-9
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2534 |
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2022-07-24,10,sku-9
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2535 |
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2022-07-31,0,sku-9
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2536 |
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2022-08-07,30,sku-9
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2537 |
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2022-08-14,6,sku-9
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2538 |
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2022-08-21,6,sku-9
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2539 |
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2022-08-28,10,sku-9
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2540 |
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2022-09-04,27,sku-9
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2541 |
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2022-09-11,0,sku-9
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2542 |
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2022-09-18,10,sku-9
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2543 |
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2022-09-25,10,sku-9
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2544 |
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2022-10-02,10,sku-9
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2545 |
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2022-10-09,0,sku-9
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2546 |
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2022-10-16,0,sku-9
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2547 |
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2022-10-23,10,sku-9
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2548 |
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2022-10-30,20,sku-9
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2549 |
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2022-11-06,0,sku-9
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2550 |
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2022-11-13,0,sku-9
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2551 |
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2022-11-20,50,sku-9
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2552 |
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2022-11-27,0,sku-9
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2553 |
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2022-12-04,20,sku-9
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2554 |
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2022-12-11,20,sku-9
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2555 |
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2022-12-18,30,sku-9
|
2556 |
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2022-12-25,20,sku-9
|
2557 |
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2023-01-01,15,sku-9
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2558 |
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2023-01-08,0,sku-9
|
2559 |
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2023-01-15,5,sku-9
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2560 |
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2023-01-22,0,sku-9
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2561 |
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2023-01-29,20,sku-9
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2562 |
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2023-02-05,25,sku-9
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2563 |
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2023-02-12,10,sku-9
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2564 |
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2023-02-19,30,sku-9
|
2565 |
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2023-02-26,25,sku-9
|
2566 |
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2023-03-05,13,sku-9
|
2567 |
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2023-03-12,15,sku-9
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2568 |
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2023-03-19,5,sku-9
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2569 |
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2023-03-26,0,sku-9
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2570 |
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2023-04-02,30,sku-9
|
2571 |
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2023-04-09,2,sku-9
|
2572 |
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2023-04-16,30,sku-9
|
2573 |
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2023-04-23,10,sku-9
|
data/demand_forecasting_demo_models.csv
ADDED
@@ -0,0 +1,11 @@
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|
1 |
+
sku,best_model,characteristic,RMSE
|
2 |
+
sku-0,fft_plus,continuous,20.29778313018444
|
3 |
+
sku-1,holt_winters_plus,continuous,48.49842843820416
|
4 |
+
sku-2,prophet_plus,fuzzy,39.28846310729568
|
5 |
+
sku-3,prophet_plus,fuzzy_transient,14.593201789242087
|
6 |
+
sku-4,prophet_plus,fuzzy,10.7747925198657
|
7 |
+
sku-5,prophet_plus,fuzzy,28.33012802382216
|
8 |
+
sku-6,ceif_plus,fuzzy,37.84242038358283
|
9 |
+
sku-7,holt_winters_plus,continuous,15.959770854065722
|
10 |
+
sku-8,prophet_plus,fuzzy,13.778467035419936
|
11 |
+
sku-9,prophet_plus,fuzzy,12.843706019437128
|
data/fuzzy.csv
ADDED
@@ -0,0 +1,261 @@
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|
1 |
+
datetime,y
|
2 |
+
2018-05-06,2
|
3 |
+
2018-05-13,12
|
4 |
+
2018-05-20,6
|
5 |
+
2018-05-27,9
|
6 |
+
2018-06-03,5
|
7 |
+
2018-06-10,2
|
8 |
+
2018-06-17,0
|
9 |
+
2018-06-24,3
|
10 |
+
2018-07-01,1
|
11 |
+
2018-07-08,6
|
12 |
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2018-07-15,9
|
13 |
+
2018-07-22,9
|
14 |
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2018-07-29,9
|
15 |
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2018-08-05,8
|
16 |
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2018-08-12,1
|
17 |
+
2018-08-19,0
|
18 |
+
2018-08-26,2
|
19 |
+
2018-09-02,11
|
20 |
+
2018-09-09,9
|
21 |
+
2018-09-16,4
|
22 |
+
2018-09-23,24
|
23 |
+
2018-09-30,13
|
24 |
+
2018-10-07,0
|
25 |
+
2018-10-14,0
|
26 |
+
2018-10-21,0
|
27 |
+
2018-10-28,6
|
28 |
+
2018-11-04,25
|
29 |
+
2018-11-11,0
|
30 |
+
2018-11-18,12
|
31 |
+
2018-11-25,5
|
32 |
+
2018-12-02,11
|
33 |
+
2018-12-09,4
|
34 |
+
2018-12-16,2
|
35 |
+
2018-12-23,4
|
36 |
+
2018-12-30,0
|
37 |
+
2019-01-06,0
|
38 |
+
2019-01-13,4
|
39 |
+
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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2021-03-21,10
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|
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2021-04-04,0
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2021-04-11,0
|
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2021-04-18,0
|
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2021-04-25,30
|
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2021-05-02,9
|
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|
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|
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|
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|
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2021-06-27,32
|
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2021-07-04,0
|
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2021-07-11,10
|
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2021-07-18,10
|
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2021-07-25,0
|
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2021-08-01,0
|
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2021-08-08,0
|
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2021-08-15,0
|
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2021-08-22,0
|
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2021-08-29,0
|
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2021-09-05,0
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2021-09-12,15
|
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2021-09-19,10
|
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2021-09-26,5
|
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2021-10-03,0
|
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2021-10-10,24
|
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2021-10-17,18
|
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2021-10-24,6
|
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2021-10-31,7
|
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2021-11-07,8
|
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2021-11-14,25
|
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2021-11-21,10
|
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2021-11-28,10
|
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2021-12-05,2
|
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2021-12-12,2
|
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2021-12-19,0
|
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2021-12-26,0
|
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2022-01-02,2
|
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2022-01-09,4
|
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2022-01-16,3
|
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2022-01-23,10
|
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2022-01-30,10
|
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2022-02-06,0
|
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2022-02-13,20
|
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2022-02-20,25
|
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2022-02-27,10
|
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2022-03-06,29
|
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2022-03-13,10
|
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2022-03-20,7
|
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2022-03-27,24
|
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2022-04-03,3
|
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2022-04-10,10
|
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2022-04-17,7
|
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2022-04-24,2
|
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2022-05-01,0
|
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+
2022-05-08,0
|
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+
2022-05-15,10
|
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2022-05-22,7
|
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2022-05-29,9
|
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2022-06-05,6
|
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2022-06-12,5
|
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+
2022-06-19,35
|
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+
2022-06-26,20
|
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2022-07-03,0
|
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2022-07-10,5
|
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2022-07-17,5
|
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2022-07-24,9
|
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2022-07-31,14
|
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2022-08-07,20
|
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2022-08-14,10
|
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2022-08-21,10
|
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2022-08-28,1
|
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+
2022-09-04,15
|
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2022-09-11,22
|
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+
2022-09-18,10
|
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+
2022-09-25,10
|
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+
2022-10-02,20
|
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+
2022-10-09,0
|
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+
2022-10-16,0
|
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+
2022-10-23,0
|
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+
2022-10-30,15
|
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+
2022-11-06,10
|
238 |
+
2022-11-13,0
|
239 |
+
2022-11-20,10
|
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+
2022-11-27,10
|
241 |
+
2022-12-04,0
|
242 |
+
2022-12-11,0
|
243 |
+
2022-12-18,0
|
244 |
+
2022-12-25,7
|
245 |
+
2023-01-01,10
|
246 |
+
2023-01-08,10
|
247 |
+
2023-01-15,0
|
248 |
+
2023-01-22,5
|
249 |
+
2023-01-29,0
|
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+
2023-02-05,7
|
251 |
+
2023-02-12,2
|
252 |
+
2023-02-19,0
|
253 |
+
2023-02-26,20
|
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+
2023-03-05,13
|
255 |
+
2023-03-12,10
|
256 |
+
2023-03-19,0
|
257 |
+
2023-03-26,0
|
258 |
+
2023-04-02,10
|
259 |
+
2023-04-09,8
|
260 |
+
2023-04-16,10
|
261 |
+
2023-04-23,5
|
data/fuzzy_2.csv
ADDED
@@ -0,0 +1,37 @@
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
datetime,y
|
2 |
+
2020-01-31,50.0
|
3 |
+
2020-02-29,0.0
|
4 |
+
2020-03-31,300.0
|
5 |
+
2020-04-30,0.0
|
6 |
+
2020-05-31,500.0
|
7 |
+
2020-06-30,1000.0
|
8 |
+
2020-07-31,1000.0
|
9 |
+
2020-08-31,500.0
|
10 |
+
2020-09-30,0.0
|
11 |
+
2020-10-31,1000.0
|
12 |
+
2020-11-30,1000.0
|
13 |
+
2020-12-31,500.0
|
14 |
+
2021-01-31,525.0
|
15 |
+
2021-02-28,750.0
|
16 |
+
2021-03-31,250.0
|
17 |
+
2021-04-30,0.0
|
18 |
+
2021-05-31,0.0
|
19 |
+
2021-06-30,975.0
|
20 |
+
2021-07-31,975.0
|
21 |
+
2021-08-31,1550.0
|
22 |
+
2021-09-30,1309.0
|
23 |
+
2021-10-31,2450.0
|
24 |
+
2021-11-30,2360.0
|
25 |
+
2021-12-31,3670.0
|
26 |
+
2022-01-31,5530.0
|
27 |
+
2022-02-28,2990.0
|
28 |
+
2022-03-31,1050.0
|
29 |
+
2022-04-30,2750.0
|
30 |
+
2022-05-31,6124.0
|
31 |
+
2022-06-30,3510.0
|
32 |
+
2022-07-31,4000.0
|
33 |
+
2022-08-31,3400.0
|
34 |
+
2022-09-30,2500.0
|
35 |
+
2022-10-31,3000.0
|
36 |
+
2022-11-30,3800.0
|
37 |
+
2022-12-31,2560.0
|
data/resource.md
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
test.csv came from SKU 8972413061 from CID016 data from Isuzu
|
demo.py
ADDED
@@ -0,0 +1,121 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
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|
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|
|
|
|
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|
|
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|
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|
|
|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
|
3 |
+
# from arguments import init_args
|
4 |
+
from gr_app.GradioApp import GradioApp
|
5 |
+
from gr_app import args
|
6 |
+
|
7 |
+
app = GradioApp()
|
8 |
+
|
9 |
+
demo = gr.Blocks(**args.block)
|
10 |
+
|
11 |
+
with demo:
|
12 |
+
warning = gr.Warning()
|
13 |
+
gr.Markdown('# Sentient.io - Demand Forecasting')
|
14 |
+
gr.Markdown('Demo for demand forecasting pipeline')
|
15 |
+
|
16 |
+
gr.Markdown('---')
|
17 |
+
|
18 |
+
gr.Markdown('# Step 1 - Load Data')
|
19 |
+
with gr.Row():
|
20 |
+
gr.Markdown('''
|
21 |
+
Use button "Load Demo Data" for a quick demo with pre-loaded data. For uploading your own data, please follow the below requirements.
|
22 |
+
|
23 |
+
### Data Requirements:
|
24 |
+
- Time series data have to be in CSV format
|
25 |
+
- Data must contains datetime, y and sku columns.
|
26 |
+
- Multiple SKUs can put in to same CSV
|
27 |
+
- Time interval in data must be consistent
|
28 |
+
- Missing value have to be filled up
|
29 |
+
''')
|
30 |
+
|
31 |
+
with gr.Column():
|
32 |
+
btn_load_data = gr.Button('Load Demo Data')
|
33 |
+
|
34 |
+
gr.Markdown('------ or ------',
|
35 |
+
elem_classes="demo_app_text_center")
|
36 |
+
|
37 |
+
file_upload_data = gr.File(**args.file_upload_data)
|
38 |
+
|
39 |
+
df_ts_data = gr.DataFrame(**args.df_ts_data)
|
40 |
+
|
41 |
+
gr.Markdown('---')
|
42 |
+
|
43 |
+
gr.Markdown('# Step 2 - Model Selection')
|
44 |
+
|
45 |
+
with gr.Row():
|
46 |
+
gr.Markdown('''
|
47 |
+
Train and evaluate model, identify data characteristics and select the best performing model. This step only need to run when the market regime shifted or when need to to re-select the model.
|
48 |
+
|
49 |
+
- Click "Use Demo Data" Button if the demo data set has been loaded in Step 1
|
50 |
+
- Else, directly proceed to model selection
|
51 |
+
- Only upload dataset if the model select process had been previously done, and you have save a copy of the CSV response.
|
52 |
+
''')
|
53 |
+
|
54 |
+
with gr.Column():
|
55 |
+
btn_load_model_data = gr.Button('Use Demo Data')
|
56 |
+
btn_model_selection = gr.Button('Model Selection', variant='primary')
|
57 |
+
gr.Markdown('Upload previous model selection result (if have):')
|
58 |
+
file_upload_model_data = gr.File(**args.file_upload_model_data)
|
59 |
+
|
60 |
+
df_model_data = gr.DataFrame()
|
61 |
+
file_model_data = gr.File()
|
62 |
+
|
63 |
+
gr.Markdown('# Step 3 - Forecasting')
|
64 |
+
|
65 |
+
with gr.Row():
|
66 |
+
gr.Markdown(
|
67 |
+
'This step only can be done when model selection process is completed.')
|
68 |
+
|
69 |
+
with gr.Column():
|
70 |
+
gr.Markdown('''
|
71 |
+
### Forecast Horizon
|
72 |
+
Max horizon will be 20% of provided data range. The unit will be same as the time series data time interval.
|
73 |
+
''')
|
74 |
+
slider_forecast_horizon = gr.Slider(**args.slider_forecast_horizon)
|
75 |
+
|
76 |
+
btn_forecast = gr.Button("Forecast", variant='primary')
|
77 |
+
|
78 |
+
df_forecast = gr.DataFrame(**args.df_forecast)
|
79 |
+
file_forecast = gr.File()
|
80 |
+
|
81 |
+
# ============= #
|
82 |
+
# = Functions = #
|
83 |
+
# ============= #
|
84 |
+
|
85 |
+
btn_load_data.click(
|
86 |
+
app.btn_load_data__click,
|
87 |
+
[],
|
88 |
+
[df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])
|
89 |
+
|
90 |
+
file_upload_data.upload(
|
91 |
+
app.file_upload_data__upload,
|
92 |
+
[file_upload_data],
|
93 |
+
[df_ts_data, df_model_data, file_model_data, slider_forecast_horizon])
|
94 |
+
|
95 |
+
file_upload_model_data.upload(
|
96 |
+
app.file_upload_model_data__upload,
|
97 |
+
[file_upload_model_data],
|
98 |
+
[df_model_data, file_model_data]
|
99 |
+
)
|
100 |
+
|
101 |
+
btn_load_model_data.click(
|
102 |
+
app.btn_load_model_data__click,
|
103 |
+
[], [df_model_data, file_model_data]
|
104 |
+
)
|
105 |
+
|
106 |
+
btn_model_selection.click(
|
107 |
+
app.btn_model_selection__click,
|
108 |
+
[], [df_model_data, file_model_data])
|
109 |
+
|
110 |
+
btn_forecast.click(
|
111 |
+
app.btn_forecast__click,
|
112 |
+
[], [df_forecast, file_forecast]
|
113 |
+
)
|
114 |
+
|
115 |
+
slider_forecast_horizon.change(
|
116 |
+
app.slider_forecast_horizon__update,
|
117 |
+
[slider_forecast_horizon],
|
118 |
+
[])
|
119 |
+
|
120 |
+
|
121 |
+
demo.launch()
|
environment.yml
ADDED
@@ -0,0 +1,222 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
name: demand-forecasting
|
2 |
+
channels:
|
3 |
+
- plotly
|
4 |
+
- conda-forge
|
5 |
+
- anaconda
|
6 |
+
- defaults
|
7 |
+
dependencies:
|
8 |
+
- aiofiles=22.1.0
|
9 |
+
- aiosqlite=0.18.0
|
10 |
+
- anyio=3.5.0
|
11 |
+
# - appnope=0.1.2
|
12 |
+
- argon2-cffi=21.3.0
|
13 |
+
- argon2-cffi-bindings=21.2.0
|
14 |
+
- asttokens=2.0.5
|
15 |
+
- attrs=22.1.0
|
16 |
+
- babel=2.11.0
|
17 |
+
- backcall=0.2.0
|
18 |
+
- beautifulsoup4=4.12.2
|
19 |
+
- blas=1.0
|
20 |
+
- bleach=4.1.0
|
21 |
+
- bottleneck=1.3.5
|
22 |
+
- brotli=1.0.9
|
23 |
+
- brotli-bin=1.0.9
|
24 |
+
- brotlipy=0.7.0
|
25 |
+
- bzip2=1.0.8
|
26 |
+
- ca-certificates=2022.4.26
|
27 |
+
- cctools_osx-arm64=949.0.1
|
28 |
+
- certifi=2022.6.15
|
29 |
+
- cffi=1.15.1
|
30 |
+
- charset-normalizer=2.0.4
|
31 |
+
- clang=14.0.6
|
32 |
+
- clang-14=14.0.6
|
33 |
+
- clang_osx-arm64=14.0.6
|
34 |
+
- clangxx=14.0.6
|
35 |
+
- clangxx_osx-arm64=14.0.6
|
36 |
+
- cmdstan=2.31.0
|
37 |
+
- cmdstanpy=1.1.0
|
38 |
+
- comm=0.1.2
|
39 |
+
- compiler-rt=14.0.6
|
40 |
+
- compiler-rt_osx-arm64=14.0.6
|
41 |
+
- convertdate=2.3.2
|
42 |
+
- cryptography=41.0.3
|
43 |
+
- cycler=0.11.0
|
44 |
+
- debugpy=1.6.7
|
45 |
+
- decorator=5.1.1
|
46 |
+
- defusedxml=0.7.1
|
47 |
+
- entrypoints=0.4
|
48 |
+
- ephem=4.1.2
|
49 |
+
- exceptiongroup=1.0.4
|
50 |
+
- executing=0.8.3
|
51 |
+
- freetype=2.12.1
|
52 |
+
- giflib=5.2.1
|
53 |
+
- holidays=0.29
|
54 |
+
- icu=73.1
|
55 |
+
- idna=3.4
|
56 |
+
- importlib_resources=5.2.0
|
57 |
+
- ipykernel=6.25.0
|
58 |
+
- ipython=8.15.0
|
59 |
+
- ipython_genutils=0.2.0
|
60 |
+
- jedi=0.18.1
|
61 |
+
- jinja2=3.1.2
|
62 |
+
- joblib=1.3.2
|
63 |
+
- jpeg=9e
|
64 |
+
- json5=0.9.6
|
65 |
+
- jsonschema=4.17.3
|
66 |
+
- jupyter_client=7.4.9
|
67 |
+
- jupyter_core=5.3.0
|
68 |
+
- jupyter_events=0.6.3
|
69 |
+
- jupyter_server=1.23.4
|
70 |
+
- jupyter_server_fileid=0.9.0
|
71 |
+
- jupyter_server_ydoc=0.8.0
|
72 |
+
- jupyter_ydoc=0.2.4
|
73 |
+
- jupyterlab=3.6.3
|
74 |
+
- jupyterlab_pygments=0.1.2
|
75 |
+
- jupyterlab_server=2.22.0
|
76 |
+
- lcms2=2.12
|
77 |
+
- ld64_osx-arm64=530
|
78 |
+
- ldid=2.1.5
|
79 |
+
- lerc=3.0
|
80 |
+
- libbrotlicommon=1.0.9
|
81 |
+
- libbrotlidec=1.0.9
|
82 |
+
- libbrotlienc=1.0.9
|
83 |
+
- libclang-cpp14=14.0.6
|
84 |
+
- libcxx=14.0.6
|
85 |
+
- libdeflate=1.17
|
86 |
+
- libffi=3.4.4
|
87 |
+
# - libgfortran=5.0.0
|
88 |
+
- libgfortran5=11.3.0
|
89 |
+
- libiconv=1.16
|
90 |
+
- libllvm14=14.0.6
|
91 |
+
- libopenblas=0.3.21
|
92 |
+
- libpng=1.6.39
|
93 |
+
- libsodium=1.0.18
|
94 |
+
- libtiff=4.5.1
|
95 |
+
- libwebp=1.3.2
|
96 |
+
- libwebp-base=1.3.2
|
97 |
+
- libxml2=2.10.4
|
98 |
+
- libxslt=1.1.37
|
99 |
+
- llvm-openmp=14.0.6
|
100 |
+
- llvm-tools=14.0.6
|
101 |
+
- lunarcalendar=0.0.9
|
102 |
+
- lxml=4.9.3
|
103 |
+
- lz4-c=1.9.4
|
104 |
+
- make=4.3
|
105 |
+
- markupsafe=2.1.1
|
106 |
+
- matplotlib=3.7.2
|
107 |
+
- matplotlib-base=3.7.2
|
108 |
+
- matplotlib-inline=0.1.6
|
109 |
+
- mistune=0.8.4
|
110 |
+
- munkres=1.1.4
|
111 |
+
- nbclassic=0.5.5
|
112 |
+
- nbclient=0.5.13
|
113 |
+
- nbconvert=6.5.4
|
114 |
+
- nbformat=5.9.2
|
115 |
+
- ncurses=6.4
|
116 |
+
- nest-asyncio=1.5.6
|
117 |
+
- notebook=6.5.4
|
118 |
+
- notebook-shim=0.2.2
|
119 |
+
- numexpr=2.8.4
|
120 |
+
- numpy=1.25.2
|
121 |
+
- numpy-base=1.25.2
|
122 |
+
- openssl=3.1.3
|
123 |
+
- packaging=23.1
|
124 |
+
- pandocfilters=1.5.0
|
125 |
+
- parso=0.8.3
|
126 |
+
- pexpect=4.8.0
|
127 |
+
- pickleshare=0.7.5
|
128 |
+
- pip=23.2.1
|
129 |
+
- platformdirs=3.10.0
|
130 |
+
- plotly=5.16.1
|
131 |
+
- prometheus_client=0.14.1
|
132 |
+
- prompt-toolkit=3.0.36
|
133 |
+
- prophet=1.1.4
|
134 |
+
- psutil=5.9.0
|
135 |
+
- ptyprocess=0.7.0
|
136 |
+
- pure_eval=0.2.2
|
137 |
+
- pycparser=2.21
|
138 |
+
- pygments=2.15.1
|
139 |
+
- pymeeus=0.5.11
|
140 |
+
- pyopenssl=23.2.0
|
141 |
+
- pyparsing=3.0.9
|
142 |
+
- pyrsistent=0.18.0
|
143 |
+
- pysocks=1.7.1
|
144 |
+
- python=3.10.12
|
145 |
+
- python-dateutil=2.8.2
|
146 |
+
- python-dotenv=1.0.0
|
147 |
+
- python-fastjsonschema=2.16.2
|
148 |
+
- python-json-logger=2.0.7
|
149 |
+
- pytz=2023.3.post1
|
150 |
+
- pyyaml=6.0
|
151 |
+
- pyzmq=23.2.0
|
152 |
+
- readline=8.2
|
153 |
+
- requests=2.31.0
|
154 |
+
- rfc3339-validator=0.1.4
|
155 |
+
- rfc3986-validator=0.1.1
|
156 |
+
- scikit-learn=1.3.0
|
157 |
+
- scipy=1.11.1
|
158 |
+
- seaborn=0.11.2
|
159 |
+
- send2trash=1.8.0
|
160 |
+
- setuptools=68.0.0
|
161 |
+
- six=1.16.0
|
162 |
+
- sniffio=1.2.0
|
163 |
+
- soupsieve=2.4
|
164 |
+
- sqlite=3.41.2
|
165 |
+
- stack_data=0.2.0
|
166 |
+
- tapi=1100.0.11
|
167 |
+
- tbb=2021.8.0
|
168 |
+
- tbb-devel=2021.8.0
|
169 |
+
- tenacity=8.2.2
|
170 |
+
- terminado=0.17.1
|
171 |
+
- threadpoolctl=3.2.0
|
172 |
+
- tinycss2=1.2.1
|
173 |
+
- tk=8.6.12
|
174 |
+
- tomli=2.0.1
|
175 |
+
- tornado=6.3.2
|
176 |
+
- tqdm=4.65.0
|
177 |
+
- traitlets=5.7.1
|
178 |
+
- typing-extensions=4.7.1
|
179 |
+
- typing_extensions=4.7.1
|
180 |
+
- urllib3=1.26.16
|
181 |
+
- wcwidth=0.2.5
|
182 |
+
- webencodings=0.5.1
|
183 |
+
- websocket-client=0.58.0
|
184 |
+
- wheel=0.38.4
|
185 |
+
- xz=5.4.2
|
186 |
+
- y-py=0.5.9
|
187 |
+
- yaml=0.2.5
|
188 |
+
- ypy-websocket=0.8.2
|
189 |
+
- zeromq=4.3.4
|
190 |
+
- zipp=3.11.0
|
191 |
+
- zlib=1.2.13
|
192 |
+
- zstd=1.5.5
|
193 |
+
- pip:
|
194 |
+
- altair==5.0.1
|
195 |
+
- annotated-types==0.5.0
|
196 |
+
- click==8.1.7
|
197 |
+
- contourpy==1.1.0
|
198 |
+
- fastapi==0.101.1
|
199 |
+
- ffmpy==0.3.1
|
200 |
+
- filelock==3.12.2
|
201 |
+
- fonttools==4.42.1
|
202 |
+
- fsspec==2023.6.0
|
203 |
+
- gradio==3.41.0
|
204 |
+
- gradio-client==0.5.0
|
205 |
+
- h11==0.14.0
|
206 |
+
- httpcore==0.17.3
|
207 |
+
- httpx==0.24.1
|
208 |
+
- huggingface-hub==0.16.4
|
209 |
+
- kiwisolver==1.4.5
|
210 |
+
- orjson==3.9.5
|
211 |
+
- pandas==2.0.3
|
212 |
+
- pillow==10.0.0
|
213 |
+
- pydantic==2.3.0
|
214 |
+
- pydantic-core==2.6.3
|
215 |
+
- pydub==0.25.1
|
216 |
+
- python-multipart==0.0.6
|
217 |
+
- semantic-version==2.10.0
|
218 |
+
- starlette==0.27.0
|
219 |
+
- toolz==0.12.0
|
220 |
+
- tzdata==2023.3
|
221 |
+
- uvicorn==0.23.2
|
222 |
+
- websockets==11.0.3
|
forecast_result.csv
ADDED
@@ -0,0 +1,521 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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1 |
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265 |
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2023-05-14,0,sku-5
|
266 |
+
2023-05-21,25,sku-5
|
267 |
+
2023-05-28,0,sku-5
|
268 |
+
2023-06-04,34,sku-5
|
269 |
+
2023-06-11,0,sku-5
|
270 |
+
2023-06-18,38,sku-5
|
271 |
+
2023-06-25,0,sku-5
|
272 |
+
2023-07-02,39,sku-5
|
273 |
+
2023-07-09,0,sku-5
|
274 |
+
2023-07-16,23,sku-5
|
275 |
+
2023-07-23,0,sku-5
|
276 |
+
2023-07-30,25,sku-5
|
277 |
+
2023-08-06,28,sku-5
|
278 |
+
2023-08-13,0,sku-5
|
279 |
+
2023-08-20,25,sku-5
|
280 |
+
2023-08-27,0,sku-5
|
281 |
+
2023-09-03,35,sku-5
|
282 |
+
2023-09-10,0,sku-5
|
283 |
+
2023-09-17,38,sku-5
|
284 |
+
2023-09-24,0,sku-5
|
285 |
+
2023-10-01,39,sku-5
|
286 |
+
2023-10-08,0,sku-5
|
287 |
+
2023-10-15,24,sku-5
|
288 |
+
2023-10-22,0,sku-5
|
289 |
+
2023-10-29,26,sku-5
|
290 |
+
2023-11-05,29,sku-5
|
291 |
+
2023-11-12,0,sku-5
|
292 |
+
2023-11-19,26,sku-5
|
293 |
+
2023-11-26,0,sku-5
|
294 |
+
2023-12-03,35,sku-5
|
295 |
+
2023-12-10,0,sku-5
|
296 |
+
2023-12-17,39,sku-5
|
297 |
+
2023-12-24,0,sku-5
|
298 |
+
2023-12-31,39,sku-5
|
299 |
+
2024-01-07,0,sku-5
|
300 |
+
2024-01-14,24,sku-5
|
301 |
+
2024-01-21,0,sku-5
|
302 |
+
2024-01-28,26,sku-5
|
303 |
+
2024-02-04,29,sku-5
|
304 |
+
2024-02-11,0,sku-5
|
305 |
+
2024-02-18,26,sku-5
|
306 |
+
2024-02-25,0,sku-5
|
307 |
+
2024-03-03,35,sku-5
|
308 |
+
2024-03-10,0,sku-5
|
309 |
+
2024-03-17,39,sku-5
|
310 |
+
2024-03-24,0,sku-5
|
311 |
+
2024-03-31,40,sku-5
|
312 |
+
2024-04-07,0,sku-5
|
313 |
+
2024-04-14,25,sku-5
|
314 |
+
2023-04-16,0,sku-6
|
315 |
+
2023-04-23,0,sku-6
|
316 |
+
2023-04-30,0,sku-6
|
317 |
+
2023-05-07,65,sku-6
|
318 |
+
2023-05-14,0,sku-6
|
319 |
+
2023-05-21,0,sku-6
|
320 |
+
2023-05-28,0,sku-6
|
321 |
+
2023-06-04,65,sku-6
|
322 |
+
2023-06-11,0,sku-6
|
323 |
+
2023-06-18,0,sku-6
|
324 |
+
2023-06-25,0,sku-6
|
325 |
+
2023-07-02,39,sku-6
|
326 |
+
2023-07-09,0,sku-6
|
327 |
+
2023-07-16,0,sku-6
|
328 |
+
2023-07-23,0,sku-6
|
329 |
+
2023-07-30,40,sku-6
|
330 |
+
2023-08-06,0,sku-6
|
331 |
+
2023-08-13,0,sku-6
|
332 |
+
2023-08-20,0,sku-6
|
333 |
+
2023-08-27,62,sku-6
|
334 |
+
2023-09-03,0,sku-6
|
335 |
+
2023-09-10,0,sku-6
|
336 |
+
2023-09-17,0,sku-6
|
337 |
+
2023-09-24,70,sku-6
|
338 |
+
2023-10-01,0,sku-6
|
339 |
+
2023-10-08,0,sku-6
|
340 |
+
2023-10-15,0,sku-6
|
341 |
+
2023-10-22,0,sku-6
|
342 |
+
2023-10-29,0,sku-6
|
343 |
+
2023-11-05,38,sku-6
|
344 |
+
2023-11-12,0,sku-6
|
345 |
+
2023-11-19,0,sku-6
|
346 |
+
2023-11-26,0,sku-6
|
347 |
+
2023-12-03,63,sku-6
|
348 |
+
2023-12-10,0,sku-6
|
349 |
+
2023-12-17,0,sku-6
|
350 |
+
2023-12-24,0,sku-6
|
351 |
+
2023-12-31,0,sku-6
|
352 |
+
2024-01-07,0,sku-6
|
353 |
+
2024-01-14,0,sku-6
|
354 |
+
2024-01-21,0,sku-6
|
355 |
+
2024-01-28,0,sku-6
|
356 |
+
2024-02-04,44,sku-6
|
357 |
+
2024-02-11,0,sku-6
|
358 |
+
2024-02-18,0,sku-6
|
359 |
+
2024-02-25,0,sku-6
|
360 |
+
2024-03-03,0,sku-6
|
361 |
+
2024-03-10,61,sku-6
|
362 |
+
2024-03-17,0,sku-6
|
363 |
+
2024-03-24,0,sku-6
|
364 |
+
2024-03-31,0,sku-6
|
365 |
+
2024-04-07,40,sku-6
|
366 |
+
2023-04-23,17,sku-7
|
367 |
+
2023-04-30,17,sku-7
|
368 |
+
2023-05-07,17,sku-7
|
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+
2023-05-14,17,sku-7
|
370 |
+
2023-05-21,17,sku-7
|
371 |
+
2023-05-28,17,sku-7
|
372 |
+
2023-06-04,17,sku-7
|
373 |
+
2023-06-11,17,sku-7
|
374 |
+
2023-06-18,17,sku-7
|
375 |
+
2023-06-25,17,sku-7
|
376 |
+
2023-07-02,17,sku-7
|
377 |
+
2023-07-09,17,sku-7
|
378 |
+
2023-07-16,17,sku-7
|
379 |
+
2023-07-23,17,sku-7
|
380 |
+
2023-07-30,17,sku-7
|
381 |
+
2023-08-06,17,sku-7
|
382 |
+
2023-08-13,17,sku-7
|
383 |
+
2023-08-20,17,sku-7
|
384 |
+
2023-08-27,17,sku-7
|
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+
2023-09-03,17,sku-7
|
386 |
+
2023-09-10,17,sku-7
|
387 |
+
2023-09-17,17,sku-7
|
388 |
+
2023-09-24,17,sku-7
|
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+
2023-10-01,17,sku-7
|
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+
2023-10-08,17,sku-7
|
391 |
+
2023-10-15,17,sku-7
|
392 |
+
2023-10-22,17,sku-7
|
393 |
+
2023-10-29,17,sku-7
|
394 |
+
2023-11-05,17,sku-7
|
395 |
+
2023-11-12,17,sku-7
|
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+
2023-11-19,17,sku-7
|
397 |
+
2023-11-26,17,sku-7
|
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+
2023-12-03,17,sku-7
|
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+
2023-12-10,17,sku-7
|
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+
2023-12-17,17,sku-7
|
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2023-12-24,17,sku-7
|
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+
2023-12-31,17,sku-7
|
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+
2024-01-07,17,sku-7
|
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+
2024-01-14,17,sku-7
|
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+
2024-01-21,17,sku-7
|
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+
2024-01-28,17,sku-7
|
407 |
+
2024-02-04,17,sku-7
|
408 |
+
2024-02-11,17,sku-7
|
409 |
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2024-02-18,17,sku-7
|
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+
2024-02-25,17,sku-7
|
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+
2024-03-03,17,sku-7
|
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+
2024-03-10,17,sku-7
|
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2024-03-17,17,sku-7
|
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+
2024-03-24,17,sku-7
|
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2024-03-31,17,sku-7
|
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2024-04-07,17,sku-7
|
417 |
+
2024-04-14,17,sku-7
|
418 |
+
2023-04-23,15,sku-8
|
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+
2023-04-30,0,sku-8
|
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+
2023-05-07,16,sku-8
|
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+
2023-05-14,18,sku-8
|
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+
2023-05-21,0,sku-8
|
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+
2023-05-28,17,sku-8
|
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2023-06-04,15,sku-8
|
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2023-06-11,0,sku-8
|
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2023-06-18,17,sku-8
|
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2023-06-25,13,sku-8
|
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+
2023-07-02,16,sku-8
|
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2023-07-09,0,sku-8
|
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2023-07-16,16,sku-8
|
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2023-07-23,18,sku-8
|
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2023-07-30,0,sku-8
|
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2023-08-06,18,sku-8
|
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2023-08-13,15,sku-8
|
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+
2023-08-20,0,sku-8
|
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2023-08-27,17,sku-8
|
437 |
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2023-09-03,13,sku-8
|
438 |
+
2023-09-10,16,sku-8
|
439 |
+
2023-09-17,0,sku-8
|
440 |
+
2023-09-24,16,sku-8
|
441 |
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2023-10-01,18,sku-8
|
442 |
+
2023-10-08,0,sku-8
|
443 |
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2023-10-15,18,sku-8
|
444 |
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2023-10-22,15,sku-8
|
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+
2023-10-29,0,sku-8
|
446 |
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2023-11-05,18,sku-8
|
447 |
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2023-11-12,14,sku-8
|
448 |
+
2023-11-19,17,sku-8
|
449 |
+
2023-11-26,0,sku-8
|
450 |
+
2023-12-03,17,sku-8
|
451 |
+
2023-12-10,19,sku-8
|
452 |
+
2023-12-17,0,sku-8
|
453 |
+
2023-12-24,18,sku-8
|
454 |
+
2023-12-31,16,sku-8
|
455 |
+
2024-01-07,0,sku-8
|
456 |
+
2024-01-14,18,sku-8
|
457 |
+
2024-01-21,14,sku-8
|
458 |
+
2024-01-28,17,sku-8
|
459 |
+
2024-02-04,0,sku-8
|
460 |
+
2024-02-11,17,sku-8
|
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+
2024-02-18,19,sku-8
|
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+
2024-02-25,0,sku-8
|
463 |
+
2024-03-03,19,sku-8
|
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+
2024-03-10,16,sku-8
|
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+
2024-03-17,0,sku-8
|
466 |
+
2024-03-24,18,sku-8
|
467 |
+
2024-03-31,14,sku-8
|
468 |
+
2024-04-07,17,sku-8
|
469 |
+
2024-04-14,0,sku-8
|
470 |
+
2023-04-23,0,sku-9
|
471 |
+
2023-04-30,19,sku-9
|
472 |
+
2023-05-07,0,sku-9
|
473 |
+
2023-05-14,17,sku-9
|
474 |
+
2023-05-21,0,sku-9
|
475 |
+
2023-05-28,21,sku-9
|
476 |
+
2023-06-04,0,sku-9
|
477 |
+
2023-06-11,19,sku-9
|
478 |
+
2023-06-18,0,sku-9
|
479 |
+
2023-06-25,17,sku-9
|
480 |
+
2023-07-02,0,sku-9
|
481 |
+
2023-07-09,19,sku-9
|
482 |
+
2023-07-16,0,sku-9
|
483 |
+
2023-07-23,19,sku-9
|
484 |
+
2023-07-30,0,sku-9
|
485 |
+
2023-08-06,20,sku-9
|
486 |
+
2023-08-13,0,sku-9
|
487 |
+
2023-08-20,18,sku-9
|
488 |
+
2023-08-27,0,sku-9
|
489 |
+
2023-09-03,21,sku-9
|
490 |
+
2023-09-10,0,sku-9
|
491 |
+
2023-09-17,20,sku-9
|
492 |
+
2023-09-24,0,sku-9
|
493 |
+
2023-10-01,18,sku-9
|
494 |
+
2023-10-08,0,sku-9
|
495 |
+
2023-10-15,20,sku-9
|
496 |
+
2023-10-22,0,sku-9
|
497 |
+
2023-10-29,20,sku-9
|
498 |
+
2023-11-05,0,sku-9
|
499 |
+
2023-11-12,20,sku-9
|
500 |
+
2023-11-19,0,sku-9
|
501 |
+
2023-11-26,18,sku-9
|
502 |
+
2023-12-03,0,sku-9
|
503 |
+
2023-12-10,22,sku-9
|
504 |
+
2023-12-17,0,sku-9
|
505 |
+
2023-12-24,20,sku-9
|
506 |
+
2023-12-31,0,sku-9
|
507 |
+
2024-01-07,18,sku-9
|
508 |
+
2024-01-14,0,sku-9
|
509 |
+
2024-01-21,20,sku-9
|
510 |
+
2024-01-28,0,sku-9
|
511 |
+
2024-02-04,20,sku-9
|
512 |
+
2024-02-11,0,sku-9
|
513 |
+
2024-02-18,21,sku-9
|
514 |
+
2024-02-25,0,sku-9
|
515 |
+
2024-03-03,19,sku-9
|
516 |
+
2024-03-10,0,sku-9
|
517 |
+
2024-03-17,22,sku-9
|
518 |
+
2024-03-24,0,sku-9
|
519 |
+
2024-03-31,21,sku-9
|
520 |
+
2024-04-07,0,sku-9
|
521 |
+
2024-04-14,19,sku-9
|
gr_app/GradioApp.py
ADDED
@@ -0,0 +1,162 @@
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|
1 |
+
import pandas as pd
|
2 |
+
import math
|
3 |
+
from src.main import DemandForecasting
|
4 |
+
|
5 |
+
import gradio as gr
|
6 |
+
|
7 |
+
|
8 |
+
class GradioApp():
|
9 |
+
def __init__(self):
|
10 |
+
self.forecaster = DemandForecasting()
|
11 |
+
|
12 |
+
self.ts_data = None # Time series data for model training and forecasting
|
13 |
+
self.model_data = None
|
14 |
+
self.skus = None
|
15 |
+
|
16 |
+
self.forecast_horizon = 1
|
17 |
+
|
18 |
+
def __set_ts_data(self, path):
|
19 |
+
self.ts_data = pd.read_csv(path)
|
20 |
+
self.skus = self.ts_data['sku'].unique()
|
21 |
+
|
22 |
+
self.model_data = pd.DataFrame(
|
23 |
+
{
|
24 |
+
'sku': self.skus,
|
25 |
+
'best_model': '',
|
26 |
+
'characteristic': '',
|
27 |
+
'RMSE': ''
|
28 |
+
}
|
29 |
+
)
|
30 |
+
|
31 |
+
def __set_model(self, model_df):
|
32 |
+
if (self.skus is None):
|
33 |
+
raise gr.Error(
|
34 |
+
'Incorrect SKUs, time series data must be loaded and SKUs must match.')
|
35 |
+
if (set(self.skus) - set(model_df['sku']) != set()):
|
36 |
+
raise gr.Error(
|
37 |
+
'SKUs in provided model select data does not match SKUs in timeseries data.'
|
38 |
+
)
|
39 |
+
|
40 |
+
self.model_data = model_df
|
41 |
+
|
42 |
+
def btn_load_data__click(self):
|
43 |
+
print('btn_load_data__click')
|
44 |
+
self.__set_ts_data('./data/demand_forecasting_demo_data.csv')
|
45 |
+
|
46 |
+
return (self.update__df_ts_data(),
|
47 |
+
self.update__df_model_data(),
|
48 |
+
self.update__file_model_data(),
|
49 |
+
self.update__slider_forecast_horizon())
|
50 |
+
|
51 |
+
def file_upload_data__upload(self, file):
|
52 |
+
self.__set_ts_data(file.name)
|
53 |
+
|
54 |
+
return (self.update__df_ts_data(),
|
55 |
+
self.update__df_model_data(),
|
56 |
+
self.update__file_model_data(),
|
57 |
+
self.update__slider_forecast_horizon())
|
58 |
+
|
59 |
+
def file_upload_model_data__upload(self, file):
|
60 |
+
model_df = pd.read_csv(file.name)
|
61 |
+
self.__set_model(model_df)
|
62 |
+
|
63 |
+
return (self.update__df_model_data(),
|
64 |
+
self.update__file_model_data())
|
65 |
+
|
66 |
+
def btn_load_model_data__click(self):
|
67 |
+
|
68 |
+
model_df = pd.read_csv(
|
69 |
+
'./data/demand_forecasting_demo_models.csv')
|
70 |
+
self.__set_model(model_df)
|
71 |
+
|
72 |
+
return (self.update__df_model_data(),
|
73 |
+
self.update__file_model_data())
|
74 |
+
|
75 |
+
def btn_model_selection__click(self):
|
76 |
+
print('btn_model_selection__click')
|
77 |
+
for sku in self.skus:
|
78 |
+
print('Selecting model ', sku)
|
79 |
+
data = self.ts_data[self.ts_data['sku'] == sku]
|
80 |
+
|
81 |
+
# ----------------- #
|
82 |
+
# Feature Selection #
|
83 |
+
# ----------------- #
|
84 |
+
res = self.forecaster.forecast(
|
85 |
+
data, 0, model='all', run_test=True)
|
86 |
+
|
87 |
+
self.model_data.loc[self.model_data['sku'] ==
|
88 |
+
sku, 'characteristic'] = res['characteristic']
|
89 |
+
|
90 |
+
self.model_data.loc[self.model_data['sku'] ==
|
91 |
+
sku, 'best_model'] = res['forecast'][0]['model']
|
92 |
+
self.model_data.loc[self.model_data['sku'] ==
|
93 |
+
sku, 'RMSE'] = math.round(res['forecast'][0]['RMSE'], 2)
|
94 |
+
|
95 |
+
return (self.update__df_model_data(),
|
96 |
+
self.update__file_model_data())
|
97 |
+
|
98 |
+
def slider_forecast_horizon__update(self, slider):
|
99 |
+
# print('slider_forecast_horizon__update ', slider)
|
100 |
+
self.forecast_horizon = slider
|
101 |
+
|
102 |
+
def btn_forecast__click(self):
|
103 |
+
# ----------- #
|
104 |
+
# Forecasting #
|
105 |
+
# ----------- #
|
106 |
+
forecasts = []
|
107 |
+
for sku in self.skus:
|
108 |
+
print('Forecasting ', sku)
|
109 |
+
data = self.ts_data[self.ts_data['sku'] == sku]
|
110 |
+
|
111 |
+
# Drop sku column first, for now the pipeline doesn't take this column
|
112 |
+
data = data.drop('sku', axis=1)
|
113 |
+
|
114 |
+
model_data = self.model_data[self.model_data['sku'] == sku]
|
115 |
+
print(model_data)
|
116 |
+
model = model_data['best_model'].tolist()[0]
|
117 |
+
characteristic = model_data['characteristic'].tolist()[0]
|
118 |
+
|
119 |
+
# ----------------- #
|
120 |
+
# Feature Selection #
|
121 |
+
# ----------------- #
|
122 |
+
print(model, characteristic)
|
123 |
+
res = self.forecaster.forecast(
|
124 |
+
data, self.forecast_horizon, model=model, run_test=False, characteristic=characteristic)
|
125 |
+
forecast = pd.DataFrame(
|
126 |
+
res['forecast'][0]['forecast'], columns=['datetime', 'y'])
|
127 |
+
forecast['sku'] = sku
|
128 |
+
forecasts.append(forecast)
|
129 |
+
|
130 |
+
self.forecast = pd.concat(forecasts)
|
131 |
+
|
132 |
+
return (self.update__df_forecast(),
|
133 |
+
self.update__file_forecast())
|
134 |
+
|
135 |
+
# ======== #
|
136 |
+
# Updaters #
|
137 |
+
# ======== #
|
138 |
+
|
139 |
+
def update__file_model_data(self):
|
140 |
+
self.model_data.to_csv('./best_models.csv', index=False)
|
141 |
+
return gr.File.update(value='./best_models.csv')
|
142 |
+
|
143 |
+
def update__df_model_data(self):
|
144 |
+
return gr.DataFrame.update(value=self.model_data)
|
145 |
+
|
146 |
+
def update__df_ts_data(self):
|
147 |
+
return gr.DataFrame.update(value=self.ts_data)
|
148 |
+
|
149 |
+
def update__slider_forecast_horizon(self):
|
150 |
+
sku = self.skus[0]
|
151 |
+
|
152 |
+
max_horizon = int(
|
153 |
+
self.ts_data[self.ts_data['sku'] == sku].shape[0] * 0.2)
|
154 |
+
|
155 |
+
return gr.Slider.update(maximum=max_horizon)
|
156 |
+
|
157 |
+
def update__df_forecast(self):
|
158 |
+
return gr.DataFrame.update(self.forecast)
|
159 |
+
|
160 |
+
def update__file_forecast(self):
|
161 |
+
self.forecast.to_csv('./forecast_result.csv', index=False)
|
162 |
+
return gr.File.update(value='./forecast_result.csv')
|
gr_app/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
|
gr_app/__pycache__/GradioApp.cpython-310.pyc
ADDED
Binary file (5.04 kB). View file
|
|
gr_app/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (172 Bytes). View file
|
|
gr_app/__pycache__/args.cpython-310.pyc
ADDED
Binary file (533 Bytes). View file
|
|
gr_app/args.py
ADDED
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
block = {
|
2 |
+
'css':
|
3 |
+
'''
|
4 |
+
.demo_app_group {padding: 1rem !important; color:red}
|
5 |
+
|
6 |
+
.demo_app_text_center {text-align: center}
|
7 |
+
'''
|
8 |
+
}
|
9 |
+
|
10 |
+
df_ts_data = {'height': 200}
|
11 |
+
df_forecast = {'height': 200}
|
12 |
+
|
13 |
+
file_upload_data = {'height': 80}
|
14 |
+
file_upload_model_data = {'height': 80}
|
15 |
+
|
16 |
+
slider_forecast_horizon = {'label': '', 'minimum': 1, 'step': 1, 'interactive':True}
|
model.csv
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,sku,best_model,characteristic,RMSE
|
2 |
+
0,sku-0,fft_plus,continuous,20.29778313018444
|
3 |
+
1,sku-1,holt_winters_plus,continuous,48.49842843820416
|
4 |
+
2,sku-2,prophet_plus,fuzzy,39.28846310729568
|
5 |
+
3,sku-3,prophet_plus,fuzzy_transient,14.593201789242087
|
6 |
+
4,sku-4,prophet_plus,fuzzy,10.7747925198657
|
7 |
+
5,sku-5,prophet_plus,fuzzy,28.33012802382216
|
8 |
+
6,sku-6,ceif_plus,fuzzy,37.84242038358283
|
9 |
+
7,sku-7,holt_winters_plus,continuous,15.959770854065722
|
10 |
+
8,sku-8,prophet_plus,fuzzy,13.778467035419936
|
11 |
+
9,sku-9,prophet_plus,fuzzy,12.843706019437128
|
notebooks/res.txt
ADDED
File without changes
|
notebooks/test.ipynb
ADDED
@@ -0,0 +1,828 @@
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"/Users/qiaozhang/miniconda3/envs/demand-forecasting/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
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" from .autonotebook import tqdm as notebook_tqdm\n"
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"name": "stdout",
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"text": [
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"apikey still available, logged in\n"
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]
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}
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],
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"source": [
|
25 |
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"# To call functions outside of this folder\n",
|
26 |
+
"import sys \n",
|
27 |
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"sys.path.insert(0, '..')\n",
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"\n",
|
29 |
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"# Load libraries \n",
|
30 |
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"import pandas as pd\n",
|
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+
"import json\n",
|
32 |
+
"import matplotlib.pyplot as plt\n",
|
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+
"\n",
|
34 |
+
"# Load main demand forecasting class\n",
|
35 |
+
"from src.main import DemandForecasting\n",
|
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+
"\n",
|
37 |
+
"df = DemandForecasting()"
|
38 |
+
]
|
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|
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|
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|
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"source": [
|
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"ts = pd.read_csv('../data/fuzzy.csv')"
|
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|
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|
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{
|
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{
|
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"name": "stdout",
|
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|
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"text": [
|
58 |
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"Start profiling, note, predictability been disabled\n",
|
59 |
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"Change point detection\n",
|
60 |
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"callindg model: prophet_plus\n",
|
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"has_idsc_model\n"
|
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]
|
63 |
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}
|
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"source": [
|
66 |
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"# Step 1 - evaluate RMSE\n",
|
67 |
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"# res = df.forecast(ts, 10, model='all', run_test=False, characteristic='fuzzy')\n",
|
68 |
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"\n",
|
69 |
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"# Step 2 - forecast\n",
|
70 |
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|
71 |
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|
72 |
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" '2023-05-21', '2023-05-28', '2023-06-04', '2023-06-11',\n",
|
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" '2023-06-18', '2023-06-25', '2023-07-02', '2023-07-09',\n",
|
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" '2023-07-16', '2023-07-23', '2023-07-30', '2023-08-06',\n",
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" 'y': dict_values([0, 18, 12, 10, 12, 0, 11, 0, 11, 13, 0, 18, 12, 10, 12, 0, 11, 0, 11, 13, 0, 18, 12, 10, 12, 0, 12, 0, 11, 13])},\n",
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|
167 |
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|
168 |
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|
169 |
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|
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171 |
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|
172 |
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|
173 |
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|
174 |
+
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|
175 |
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" <td>2023-04-23</td>\n",
|
176 |
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" <td>0</td>\n",
|
177 |
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|
178 |
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" <tr>\n",
|
179 |
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|
180 |
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" <td>2023-04-30</td>\n",
|
181 |
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" <td>18</td>\n",
|
182 |
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|
183 |
+
" <tr>\n",
|
184 |
+
" <th>2</th>\n",
|
185 |
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" <td>2023-05-07</td>\n",
|
186 |
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" <td>12</td>\n",
|
187 |
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|
188 |
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|
189 |
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|
190 |
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|
191 |
+
" <td>10</td>\n",
|
192 |
+
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|
193 |
+
" <tr>\n",
|
194 |
+
" <th>4</th>\n",
|
195 |
+
" <td>2023-05-21</td>\n",
|
196 |
+
" <td>12</td>\n",
|
197 |
+
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|
198 |
+
" <tr>\n",
|
199 |
+
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|
200 |
+
" <td>2023-05-28</td>\n",
|
201 |
+
" <td>0</td>\n",
|
202 |
+
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|
203 |
+
" <tr>\n",
|
204 |
+
" <th>6</th>\n",
|
205 |
+
" <td>2023-06-04</td>\n",
|
206 |
+
" <td>11</td>\n",
|
207 |
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|
208 |
+
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|
209 |
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|
210 |
+
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|
211 |
+
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|
212 |
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|
213 |
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|
214 |
+
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|
215 |
+
" <td>2023-06-18</td>\n",
|
216 |
+
" <td>11</td>\n",
|
217 |
+
" </tr>\n",
|
218 |
+
" <tr>\n",
|
219 |
+
" <th>9</th>\n",
|
220 |
+
" <td>2023-06-25</td>\n",
|
221 |
+
" <td>13</td>\n",
|
222 |
+
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|
223 |
+
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|
224 |
+
" <th>10</th>\n",
|
225 |
+
" <td>2023-07-02</td>\n",
|
226 |
+
" <td>0</td>\n",
|
227 |
+
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|
228 |
+
" <tr>\n",
|
229 |
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" <th>11</th>\n",
|
230 |
+
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|
231 |
+
" <td>18</td>\n",
|
232 |
+
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|
233 |
+
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|
234 |
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|
235 |
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|
236 |
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" <td>12</td>\n",
|
237 |
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|
238 |
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|
239 |
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|
240 |
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|
241 |
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|
242 |
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|
243 |
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|
244 |
+
" <th>14</th>\n",
|
245 |
+
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|
246 |
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" <td>12</td>\n",
|
247 |
+
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|
248 |
+
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|
249 |
+
" <th>15</th>\n",
|
250 |
+
" <td>2023-08-06</td>\n",
|
251 |
+
" <td>0</td>\n",
|
252 |
+
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|
253 |
+
" <tr>\n",
|
254 |
+
" <th>16</th>\n",
|
255 |
+
" <td>2023-08-13</td>\n",
|
256 |
+
" <td>11</td>\n",
|
257 |
+
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|
258 |
+
" <tr>\n",
|
259 |
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" <th>17</th>\n",
|
260 |
+
" <td>2023-08-20</td>\n",
|
261 |
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" <td>0</td>\n",
|
262 |
+
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|
263 |
+
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|
264 |
+
" <th>18</th>\n",
|
265 |
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|
266 |
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" <td>11</td>\n",
|
267 |
+
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|
268 |
+
" <tr>\n",
|
269 |
+
" <th>19</th>\n",
|
270 |
+
" <td>2023-09-03</td>\n",
|
271 |
+
" <td>13</td>\n",
|
272 |
+
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|
273 |
+
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|
274 |
+
" <th>20</th>\n",
|
275 |
+
" <td>2023-09-10</td>\n",
|
276 |
+
" <td>0</td>\n",
|
277 |
+
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|
278 |
+
" <tr>\n",
|
279 |
+
" <th>21</th>\n",
|
280 |
+
" <td>2023-09-17</td>\n",
|
281 |
+
" <td>18</td>\n",
|
282 |
+
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|
283 |
+
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|
284 |
+
" <th>22</th>\n",
|
285 |
+
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|
286 |
+
" <td>12</td>\n",
|
287 |
+
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|
288 |
+
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|
289 |
+
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|
290 |
+
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|
291 |
+
" <td>10</td>\n",
|
292 |
+
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|
293 |
+
" <tr>\n",
|
294 |
+
" <th>24</th>\n",
|
295 |
+
" <td>2023-10-08</td>\n",
|
296 |
+
" <td>12</td>\n",
|
297 |
+
" </tr>\n",
|
298 |
+
" <tr>\n",
|
299 |
+
" <th>25</th>\n",
|
300 |
+
" <td>2023-10-15</td>\n",
|
301 |
+
" <td>0</td>\n",
|
302 |
+
" </tr>\n",
|
303 |
+
" <tr>\n",
|
304 |
+
" <th>26</th>\n",
|
305 |
+
" <td>2023-10-22</td>\n",
|
306 |
+
" <td>12</td>\n",
|
307 |
+
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|
308 |
+
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|
309 |
+
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|
310 |
+
" <td>2023-10-29</td>\n",
|
311 |
+
" <td>0</td>\n",
|
312 |
+
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|
313 |
+
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|
314 |
+
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|
315 |
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|
316 |
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|
317 |
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|
318 |
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|
319 |
+
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|
320 |
+
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|
321 |
+
" <td>13</td>\n",
|
322 |
+
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|
323 |
+
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|
324 |
+
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|
325 |
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|
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355 |
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"26 2023-10-22 12\n",
|
356 |
+
"27 2023-10-29 0\n",
|
357 |
+
"28 2023-11-05 11\n",
|
358 |
+
"29 2023-11-12 13"
|
359 |
+
]
|
360 |
+
},
|
361 |
+
"execution_count": 5,
|
362 |
+
"metadata": {},
|
363 |
+
"output_type": "execute_result"
|
364 |
+
}
|
365 |
+
],
|
366 |
+
"source": [
|
367 |
+
"pd.DataFrame(res['forecast'][0]['forecast'], columns=['datetime', 'y'])"
|
368 |
+
]
|
369 |
+
},
|
370 |
+
{
|
371 |
+
"cell_type": "code",
|
372 |
+
"execution_count": 7,
|
373 |
+
"metadata": {},
|
374 |
+
"outputs": [
|
375 |
+
{
|
376 |
+
"name": "stdout",
|
377 |
+
"output_type": "stream",
|
378 |
+
"text": [
|
379 |
+
"prophet_plus\n"
|
380 |
+
]
|
381 |
+
},
|
382 |
+
{
|
383 |
+
"ename": "KeyError",
|
384 |
+
"evalue": "'interm_scores'",
|
385 |
+
"output_type": "error",
|
386 |
+
"traceback": [
|
387 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
388 |
+
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
389 |
+
"Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m 3\u001b[0m \u001b[39m# print(r['RMSE'])\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[39m# print(r['order_quantity_RMSE'])\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39;49m\u001b[39minterm_scores\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m 6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m________\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 7\u001b[0m r[\u001b[39m'\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mplot(title\u001b[39m=\u001b[39mr[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m-test\u001b[39m\u001b[39m'\u001b[39m)\n",
|
390 |
+
"\u001b[0;31mKeyError\u001b[0m: 'interm_scores'"
|
391 |
+
]
|
392 |
+
}
|
393 |
+
],
|
394 |
+
"source": [
|
395 |
+
"for r in res['forecast']:\n",
|
396 |
+
" print(r['model'])\n",
|
397 |
+
" # print(r['RMSE'])\n",
|
398 |
+
" # print(r['order_quantity_RMSE'])\n",
|
399 |
+
" print(r['interm_scores'])\n",
|
400 |
+
" print('________')\n",
|
401 |
+
" r['test'].plot(title=r['model'] + '-test')"
|
402 |
+
]
|
403 |
+
},
|
404 |
+
{
|
405 |
+
"cell_type": "code",
|
406 |
+
"execution_count": 10,
|
407 |
+
"metadata": {},
|
408 |
+
"outputs": [
|
409 |
+
{
|
410 |
+
"ename": "IndexError",
|
411 |
+
"evalue": "list index out of range",
|
412 |
+
"output_type": "error",
|
413 |
+
"traceback": [
|
414 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
415 |
+
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
|
416 |
+
"\u001b[1;32m/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb#X12sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m res[\u001b[39m4\u001b[39;49m][\u001b[39m'\u001b[39m\u001b[39mtest_raw\u001b[39m\u001b[39m'\u001b[39m]\n",
|
417 |
+
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
|
418 |
+
]
|
419 |
+
}
|
420 |
+
],
|
421 |
+
"source": [
|
422 |
+
"res[4]['test_raw']"
|
423 |
+
]
|
424 |
+
},
|
425 |
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{
|
426 |
+
"cell_type": "code",
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"execution_count": null,
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428 |
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"metadata": {},
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"outputs": [
|
430 |
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{
|
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"name": "stderr",
|
432 |
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"output_type": "stream",
|
433 |
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"text": [
|
434 |
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"11:17:02 - cmdstanpy - INFO - Chain [1] start processing\n",
|
435 |
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"11:17:02 - cmdstanpy - INFO - Chain [1] done processing\n"
|
436 |
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]
|
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},
|
438 |
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{
|
439 |
+
"name": "stdout",
|
440 |
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"output_type": "stream",
|
441 |
+
"text": [
|
442 |
+
"callindg model: prophet\n",
|
443 |
+
" ds trend yhat_lower yhat_upper trend_lower trend_upper \\\n",
|
444 |
+
"260 2023-04-30 8.921487 -7.908574 21.012119 8.921487 8.921487 \n",
|
445 |
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"261 2023-05-07 8.931226 -6.567967 22.604438 8.931226 8.931226 \n",
|
446 |
+
"262 2023-05-14 8.940965 -8.081720 21.526347 8.940905 8.941007 \n",
|
447 |
+
"263 2023-05-21 8.950703 -9.985601 19.493650 8.950543 8.950831 \n",
|
448 |
+
"264 2023-05-28 8.960442 -8.813073 20.526393 8.960129 8.960702 \n",
|
449 |
+
"265 2023-06-04 8.970180 -6.966844 23.563550 8.969667 8.970604 \n",
|
450 |
+
"266 2023-06-11 8.979919 -4.608789 25.118377 8.979231 8.980524 \n",
|
451 |
+
"267 2023-06-18 8.989657 -4.180691 25.485273 8.988761 8.990488 \n",
|
452 |
+
"268 2023-06-25 8.999396 -4.686704 24.961761 8.998250 9.000445 \n",
|
453 |
+
"269 2023-07-02 9.009134 -5.880469 23.125865 9.007781 9.010505 \n",
|
454 |
+
"270 2023-07-09 9.018873 -5.090580 23.769587 9.017264 9.020526 \n",
|
455 |
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"271 2023-07-16 9.028611 -4.761036 25.748781 9.026645 9.030525 \n",
|
456 |
+
"272 2023-07-23 9.038350 -4.907963 24.736967 9.036085 9.040552 \n",
|
457 |
+
"273 2023-07-30 9.048088 -4.570258 23.745148 9.045508 9.050722 \n",
|
458 |
+
"274 2023-08-06 9.057827 -5.827376 22.932313 9.054935 9.060731 \n",
|
459 |
+
"275 2023-08-13 9.067565 -4.220147 24.956558 9.064331 9.070780 \n",
|
460 |
+
"276 2023-08-20 9.077304 -0.864349 29.034652 9.073628 9.081006 \n",
|
461 |
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"277 2023-08-27 9.087042 3.470909 32.343086 9.082870 9.091054 \n",
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"278 2023-09-03 9.096781 1.198728 30.637736 9.092259 9.101139 \n",
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"279 2023-09-10 9.106520 -1.561115 27.531066 9.101419 9.111209 \n",
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"282 2023-10-01 9.135735 -5.178492 25.012735 9.129285 9.141805 \n",
|
467 |
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|
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"284 2023-10-15 9.155212 -6.963614 21.842661 9.147855 9.162345 \n",
|
469 |
+
"285 2023-10-22 9.164951 -6.763386 23.055701 9.157131 9.172489 \n",
|
470 |
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"286 2023-10-29 9.174689 -5.170320 24.787756 9.166426 9.182695 \n",
|
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"287 2023-11-05 9.184428 -2.337336 27.885403 9.175632 9.192803 \n",
|
472 |
+
"288 2023-11-12 9.194166 -1.331529 29.762405 9.184876 9.203052 \n",
|
473 |
+
"289 2023-11-19 9.203905 -2.375162 27.729052 9.194147 9.213316 \n",
|
474 |
+
"\n",
|
475 |
+
" additive_terms additive_terms_lower additive_terms_upper yearly \\\n",
|
476 |
+
"260 -2.521643 -2.521643 -2.521643 -2.521643 \n",
|
477 |
+
"261 -1.628433 -1.628433 -1.628433 -1.628433 \n",
|
478 |
+
"262 -2.544671 -2.544671 -2.544671 -2.544671 \n",
|
479 |
+
"263 -3.980016 -3.980016 -3.980016 -3.980016 \n",
|
480 |
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"264 -3.655285 -3.655285 -3.655285 -3.655285 \n",
|
481 |
+
"265 -1.186016 -1.186016 -1.186016 -1.186016 \n",
|
482 |
+
"266 1.337140 1.337140 1.337140 1.337140 \n",
|
483 |
+
"267 1.789455 1.789455 1.789455 1.789455 \n",
|
484 |
+
"268 0.451573 0.451573 0.451573 0.451573 \n",
|
485 |
+
"269 -0.531397 -0.531397 -0.531397 -0.531397 \n",
|
486 |
+
"270 0.094160 0.094160 0.094160 0.094160 \n",
|
487 |
+
"271 1.226812 1.226812 1.226812 1.226812 \n",
|
488 |
+
"272 1.064831 1.064831 1.064831 1.064831 \n",
|
489 |
+
"273 -0.269867 -0.269867 -0.269867 -0.269867 \n",
|
490 |
+
"274 -0.559179 -0.559179 -0.559179 -0.559179 \n",
|
491 |
+
"275 1.853473 1.853473 1.853473 1.853473 \n",
|
492 |
+
"276 5.774049 5.774049 5.774049 5.774049 \n",
|
493 |
+
"277 8.102311 8.102311 8.102311 8.102311 \n",
|
494 |
+
"278 7.022347 7.022347 7.022347 7.022347 \n",
|
495 |
+
"279 3.785691 3.785691 3.785691 3.785691 \n",
|
496 |
+
"280 1.145548 1.145548 1.145548 1.145548 \n",
|
497 |
+
"281 0.423666 0.423666 0.423666 0.423666 \n",
|
498 |
+
"282 0.590499 0.590499 0.590499 0.590499 \n",
|
499 |
+
"283 0.116618 0.116618 0.116618 0.116618 \n",
|
500 |
+
"284 -0.921952 -0.921952 -0.921952 -0.921952 \n",
|
501 |
+
"285 -1.023065 -1.023065 -1.023065 -1.023065 \n",
|
502 |
+
"286 0.705503 0.705503 0.705503 0.705503 \n",
|
503 |
+
"287 3.244306 3.244306 3.244306 3.244306 \n",
|
504 |
+
"288 4.575861 4.575861 4.575861 4.575861 \n",
|
505 |
+
"289 3.595682 3.595682 3.595682 3.595682 \n",
|
506 |
+
"\n",
|
507 |
+
" yearly_lower yearly_upper multiplicative_terms \\\n",
|
508 |
+
"260 -2.521643 -2.521643 0.0 \n",
|
509 |
+
"261 -1.628433 -1.628433 0.0 \n",
|
510 |
+
"262 -2.544671 -2.544671 0.0 \n",
|
511 |
+
"263 -3.980016 -3.980016 0.0 \n",
|
512 |
+
"264 -3.655285 -3.655285 0.0 \n",
|
513 |
+
"265 -1.186016 -1.186016 0.0 \n",
|
514 |
+
"266 1.337140 1.337140 0.0 \n",
|
515 |
+
"267 1.789455 1.789455 0.0 \n",
|
516 |
+
"268 0.451573 0.451573 0.0 \n",
|
517 |
+
"269 -0.531397 -0.531397 0.0 \n",
|
518 |
+
"270 0.094160 0.094160 0.0 \n",
|
519 |
+
"271 1.226812 1.226812 0.0 \n",
|
520 |
+
"272 1.064831 1.064831 0.0 \n",
|
521 |
+
"273 -0.269867 -0.269867 0.0 \n",
|
522 |
+
"274 -0.559179 -0.559179 0.0 \n",
|
523 |
+
"275 1.853473 1.853473 0.0 \n",
|
524 |
+
"276 5.774049 5.774049 0.0 \n",
|
525 |
+
"277 8.102311 8.102311 0.0 \n",
|
526 |
+
"278 7.022347 7.022347 0.0 \n",
|
527 |
+
"279 3.785691 3.785691 0.0 \n",
|
528 |
+
"280 1.145548 1.145548 0.0 \n",
|
529 |
+
"281 0.423666 0.423666 0.0 \n",
|
530 |
+
"282 0.590499 0.590499 0.0 \n",
|
531 |
+
"283 0.116618 0.116618 0.0 \n",
|
532 |
+
"284 -0.921952 -0.921952 0.0 \n",
|
533 |
+
"285 -1.023065 -1.023065 0.0 \n",
|
534 |
+
"286 0.705503 0.705503 0.0 \n",
|
535 |
+
"287 3.244306 3.244306 0.0 \n",
|
536 |
+
"288 4.575861 4.575861 0.0 \n",
|
537 |
+
"289 3.595682 3.595682 0.0 \n",
|
538 |
+
"\n",
|
539 |
+
" multiplicative_terms_lower multiplicative_terms_upper yhat \n",
|
540 |
+
"260 0.0 0.0 6.399845 \n",
|
541 |
+
"261 0.0 0.0 7.302793 \n",
|
542 |
+
"262 0.0 0.0 6.396294 \n",
|
543 |
+
"263 0.0 0.0 4.970687 \n",
|
544 |
+
"264 0.0 0.0 5.305157 \n",
|
545 |
+
"265 0.0 0.0 7.784164 \n",
|
546 |
+
"266 0.0 0.0 10.317059 \n",
|
547 |
+
"267 0.0 0.0 10.779112 \n",
|
548 |
+
"268 0.0 0.0 9.450969 \n",
|
549 |
+
"269 0.0 0.0 8.477737 \n",
|
550 |
+
"270 0.0 0.0 9.113033 \n",
|
551 |
+
"271 0.0 0.0 10.255423 \n",
|
552 |
+
"272 0.0 0.0 10.103181 \n",
|
553 |
+
"273 0.0 0.0 8.778221 \n",
|
554 |
+
"274 0.0 0.0 8.498648 \n",
|
555 |
+
"275 0.0 0.0 10.921038 \n",
|
556 |
+
"276 0.0 0.0 14.851353 \n",
|
557 |
+
"277 0.0 0.0 17.189353 \n",
|
558 |
+
"278 0.0 0.0 16.119128 \n",
|
559 |
+
"279 0.0 0.0 12.892211 \n",
|
560 |
+
"280 0.0 0.0 10.261806 \n",
|
561 |
+
"281 0.0 0.0 9.549662 \n",
|
562 |
+
"282 0.0 0.0 9.726234 \n",
|
563 |
+
"283 0.0 0.0 9.262091 \n",
|
564 |
+
"284 0.0 0.0 8.233260 \n",
|
565 |
+
"285 0.0 0.0 8.141886 \n",
|
566 |
+
"286 0.0 0.0 9.880192 \n",
|
567 |
+
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|
568 |
+
"288 0.0 0.0 13.770027 \n",
|
569 |
+
"289 0.0 0.0 12.799587 \n"
|
570 |
+
]
|
571 |
+
}
|
572 |
+
],
|
573 |
+
"source": [
|
574 |
+
"# Step 2 - forecast\n",
|
575 |
+
"res = df.forecast(ts, 30, model='prophet')"
|
576 |
+
]
|
577 |
+
},
|
578 |
+
{
|
579 |
+
"cell_type": "code",
|
580 |
+
"execution_count": null,
|
581 |
+
"metadata": {},
|
582 |
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|
583 |
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|
584 |
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|
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+
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|
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|
609 |
+
" <th>2023-04-23</th>\n",
|
610 |
+
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|
611 |
+
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|
612 |
+
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|
613 |
+
" <th>2023-04-30</th>\n",
|
614 |
+
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|
615 |
+
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|
616 |
+
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|
617 |
+
" <th>2023-05-07</th>\n",
|
618 |
+
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|
619 |
+
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|
620 |
+
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|
621 |
+
" <th>2023-05-14</th>\n",
|
622 |
+
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|
623 |
+
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|
624 |
+
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|
625 |
+
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|
626 |
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|
627 |
+
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|
628 |
+
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|
629 |
+
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|
630 |
+
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|
631 |
+
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|
632 |
+
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|
633 |
+
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|
634 |
+
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|
635 |
+
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|
636 |
+
" <tr>\n",
|
637 |
+
" <th>2023-06-11</th>\n",
|
638 |
+
" <td>10.779112</td>\n",
|
639 |
+
" </tr>\n",
|
640 |
+
" <tr>\n",
|
641 |
+
" <th>2023-06-18</th>\n",
|
642 |
+
" <td>9.450969</td>\n",
|
643 |
+
" </tr>\n",
|
644 |
+
" <tr>\n",
|
645 |
+
" <th>2023-06-25</th>\n",
|
646 |
+
" <td>8.477737</td>\n",
|
647 |
+
" </tr>\n",
|
648 |
+
" <tr>\n",
|
649 |
+
" <th>2023-07-02</th>\n",
|
650 |
+
" <td>9.113033</td>\n",
|
651 |
+
" </tr>\n",
|
652 |
+
" <tr>\n",
|
653 |
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" <th>2023-07-09</th>\n",
|
654 |
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" <td>10.255423</td>\n",
|
655 |
+
" </tr>\n",
|
656 |
+
" <tr>\n",
|
657 |
+
" <th>2023-07-16</th>\n",
|
658 |
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" <td>10.103181</td>\n",
|
659 |
+
" </tr>\n",
|
660 |
+
" <tr>\n",
|
661 |
+
" <th>2023-07-23</th>\n",
|
662 |
+
" <td>8.778221</td>\n",
|
663 |
+
" </tr>\n",
|
664 |
+
" <tr>\n",
|
665 |
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" <th>2023-07-30</th>\n",
|
666 |
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" <td>8.498648</td>\n",
|
667 |
+
" </tr>\n",
|
668 |
+
" <tr>\n",
|
669 |
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" <th>2023-08-06</th>\n",
|
670 |
+
" <td>10.921038</td>\n",
|
671 |
+
" </tr>\n",
|
672 |
+
" <tr>\n",
|
673 |
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" <th>2023-08-13</th>\n",
|
674 |
+
" <td>14.851353</td>\n",
|
675 |
+
" </tr>\n",
|
676 |
+
" <tr>\n",
|
677 |
+
" <th>2023-08-20</th>\n",
|
678 |
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" <td>17.189353</td>\n",
|
679 |
+
" </tr>\n",
|
680 |
+
" <tr>\n",
|
681 |
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" <th>2023-08-27</th>\n",
|
682 |
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" <td>16.119128</td>\n",
|
683 |
+
" </tr>\n",
|
684 |
+
" <tr>\n",
|
685 |
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" <th>2023-09-03</th>\n",
|
686 |
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" <td>12.892211</td>\n",
|
687 |
+
" </tr>\n",
|
688 |
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" <tr>\n",
|
689 |
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" <th>2023-09-10</th>\n",
|
690 |
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" <td>10.261806</td>\n",
|
691 |
+
" </tr>\n",
|
692 |
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" <tr>\n",
|
693 |
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" <th>2023-09-17</th>\n",
|
694 |
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" <td>9.549662</td>\n",
|
695 |
+
" </tr>\n",
|
696 |
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" <tr>\n",
|
697 |
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" <th>2023-09-24</th>\n",
|
698 |
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" <td>9.726234</td>\n",
|
699 |
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" </tr>\n",
|
700 |
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" <tr>\n",
|
701 |
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" <th>2023-10-01</th>\n",
|
702 |
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" <td>9.262091</td>\n",
|
703 |
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" </tr>\n",
|
704 |
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" <tr>\n",
|
705 |
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" <th>2023-10-08</th>\n",
|
706 |
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" <td>8.233260</td>\n",
|
707 |
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" </tr>\n",
|
708 |
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" <tr>\n",
|
709 |
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" <th>2023-10-15</th>\n",
|
710 |
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" <td>8.141886</td>\n",
|
711 |
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" </tr>\n",
|
712 |
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" <tr>\n",
|
713 |
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" <th>2023-10-22</th>\n",
|
714 |
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" <td>9.880192</td>\n",
|
715 |
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" </tr>\n",
|
716 |
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" <tr>\n",
|
717 |
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" <th>2023-10-29</th>\n",
|
718 |
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" <td>12.428733</td>\n",
|
719 |
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" </tr>\n",
|
720 |
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" <tr>\n",
|
721 |
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" <th>2023-11-05</th>\n",
|
722 |
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" <td>13.770027</td>\n",
|
723 |
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" </tr>\n",
|
724 |
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" <tr>\n",
|
725 |
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" <th>2023-11-12</th>\n",
|
726 |
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" <td>12.799587</td>\n",
|
727 |
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" </tr>\n",
|
728 |
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" </tbody>\n",
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729 |
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"</table>\n",
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730 |
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731 |
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],
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" y\n",
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760 |
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761 |
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762 |
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763 |
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764 |
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]
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765 |
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},
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766 |
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"execution_count": 17,
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767 |
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"metadata": {},
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768 |
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"output_type": "execute_result"
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769 |
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}
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770 |
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],
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771 |
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"source": [
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772 |
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"res[0]['forecast']"
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773 |
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]
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774 |
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},
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775 |
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{
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776 |
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"cell_type": "code",
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777 |
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"execution_count": null,
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778 |
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"metadata": {},
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779 |
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"outputs": [
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780 |
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{
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781 |
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"data": {
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782 |
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"text/plain": [
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783 |
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"<Axes: title={'center': 'forecasted'}>"
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784 |
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]
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785 |
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},
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"execution_count": 18,
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",
|
793 |
+
"text/plain": [
|
794 |
+
"<Figure size 640x480 with 1 Axes>"
|
795 |
+
]
|
796 |
+
},
|
797 |
+
"metadata": {},
|
798 |
+
"output_type": "display_data"
|
799 |
+
}
|
800 |
+
],
|
801 |
+
"source": [
|
802 |
+
"res[0]['forecast'].plot(title='forecasted')"
|
803 |
+
]
|
804 |
+
}
|
805 |
+
],
|
806 |
+
"metadata": {
|
807 |
+
"kernelspec": {
|
808 |
+
"display_name": "demand-forecasting",
|
809 |
+
"language": "python",
|
810 |
+
"name": "python3"
|
811 |
+
},
|
812 |
+
"language_info": {
|
813 |
+
"codemirror_mode": {
|
814 |
+
"name": "ipython",
|
815 |
+
"version": 3
|
816 |
+
},
|
817 |
+
"file_extension": ".py",
|
818 |
+
"mimetype": "text/x-python",
|
819 |
+
"name": "python",
|
820 |
+
"nbconvert_exporter": "python",
|
821 |
+
"pygments_lexer": "ipython3",
|
822 |
+
"version": "3.10.12"
|
823 |
+
},
|
824 |
+
"orig_nbformat": 4
|
825 |
+
},
|
826 |
+
"nbformat": 4,
|
827 |
+
"nbformat_minor": 2
|
828 |
+
}
|
src/__init__.py
ADDED
File without changes
|
src/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (182 Bytes). View file
|
|
src/__pycache__/__init__.cpython-311.pyc
ADDED
Binary file (198 Bytes). View file
|
|
src/__pycache__/avtive_models.cpython-310.pyc
ADDED
Binary file (452 Bytes). View file
|
|
src/__pycache__/main.cpython-310.pyc
ADDED
Binary file (10.3 kB). View file
|
|
src/__pycache__/main.cpython-311.pyc
ADDED
Binary file (11 kB). View file
|
|
src/avtive_models.py
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
'''
|
2 |
+
'ceif_plus' : 2023 Sep. currently the model is under review, so not recommended to use this mode - by idsc
|
3 |
+
'''
|
4 |
+
|
5 |
+
active_models = {
|
6 |
+
'intermittent':
|
7 |
+
[
|
8 |
+
'prophet_plus',
|
9 |
+
'ceif_plus'
|
10 |
+
],
|
11 |
+
'continuous':
|
12 |
+
[
|
13 |
+
'fft_plus',
|
14 |
+
'holt_winters_plus',
|
15 |
+
'auto_arima_plus',
|
16 |
+
'prophet',
|
17 |
+
'prophet_plus',
|
18 |
+
]
|
19 |
+
}
|
src/forecast/Prophet.py
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pandas as pd
|
2 |
+
from prophet import Prophet
|
3 |
+
import numpy as np
|
4 |
+
|
5 |
+
|
6 |
+
class ProphetWrapper():
|
7 |
+
def __init__(self):
|
8 |
+
pass
|
9 |
+
|
10 |
+
def forecast(self, ts, n_predict, freq=None):
|
11 |
+
model = Prophet()
|
12 |
+
train = ts.rename(columns={'datetime': 'ds'})
|
13 |
+
|
14 |
+
model.fit(train)
|
15 |
+
|
16 |
+
future = model.make_future_dataframe(periods=n_predict, freq=freq)
|
17 |
+
|
18 |
+
forecasted = model.predict(future)
|
19 |
+
|
20 |
+
print(forecasted[-n_predict:])
|
21 |
+
|
22 |
+
return forecasted[-n_predict:]
|
src/forecast/__init__.py
ADDED
File without changes
|
src/forecast/__pycache__/Prophet.cpython-310.pyc
ADDED
Binary file (937 Bytes). View file
|
|
src/forecast/__pycache__/Prophet.cpython-311.pyc
ADDED
Binary file (1.29 kB). View file
|
|
src/forecast/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (191 Bytes). View file
|
|
src/forecast/__pycache__/__init__.cpython-311.pyc
ADDED
Binary file (207 Bytes). View file
|
|
src/functions/__init__.py
ADDED
File without changes
|
src/functions/__pycache__/__init__.cpython-310.pyc
ADDED
Binary file (192 Bytes). View file
|
|
src/functions/__pycache__/__init__.cpython-311.pyc
ADDED
Binary file (208 Bytes). View file
|
|
src/functions/__pycache__/check_input.cpython-310.pyc
ADDED
Binary file (350 Bytes). View file
|
|
src/functions/__pycache__/check_input.cpython-311.pyc
ADDED
Binary file (437 Bytes). View file
|
|
src/functions/__pycache__/itmtt_scores.cpython-310.pyc
ADDED
Binary file (759 Bytes). View file
|
|
src/functions/__pycache__/mase.cpython-310.pyc
ADDED
Binary file (516 Bytes). View file
|
|
src/functions/__pycache__/order_qty_rmse.cpython-310.pyc
ADDED
Binary file (545 Bytes). View file
|
|
src/functions/check_input.py
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pandas as pd
|
2 |
+
|
3 |
+
def check_input(df):
|
4 |
+
pd.infer_freq(df)
|
5 |
+
return
|
src/functions/itmtt_scores.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
def interm_scores(grdt_sr:list, pred_sr:list):
|
2 |
+
## this function calculates
|
3 |
+
## • Quantity score
|
4 |
+
## • Quantity rate score
|
5 |
+
## • Timing score
|
6 |
+
#print(grdt_sr, pred_sr)
|
7 |
+
lgrdt = len(grdt_sr)
|
8 |
+
assert lgrdt == len(pred_sr)
|
9 |
+
cnt_01match = 0
|
10 |
+
grdt_value = 0
|
11 |
+
pred_value = 0
|
12 |
+
cnt_grdt1 = 0
|
13 |
+
cnt_pred1 = 0
|
14 |
+
for i in range(lgrdt):
|
15 |
+
|
16 |
+
if (grdt_sr[i]==0 and pred_sr[i]==0) or (grdt_sr[i]> 0 and pred_sr[i]>0):
|
17 |
+
cnt_01match += 1
|
18 |
+
|
19 |
+
cnt_grdt1 += 1 if grdt_sr[i]>0 else 0
|
20 |
+
cnt_pred1 += 1 if pred_sr[i]>0 else 0
|
21 |
+
grdt_value += grdt_sr[i]
|
22 |
+
pred_value += pred_sr[i]
|
23 |
+
#print("Calculating:\nQuantity score, Quantity rate score, Timing score")
|
24 |
+
if cnt_grdt1 == 0 and cnt_pred1 == 0: # this indicate grdt_value=pred_value=0
|
25 |
+
return 1.0, 1.0, 1.0*cnt_01match / lgrdt
|
26 |
+
else:
|
27 |
+
return 1.0*min(cnt_grdt1, cnt_pred1)/max(cnt_grdt1, cnt_pred1),\
|
28 |
+
1.0*min(grdt_value, pred_value)/max(grdt_value, pred_value),\
|
29 |
+
1.0*cnt_01match / lgrdt
|
src/functions/mase.py
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy
|
2 |
+
|
3 |
+
|
4 |
+
def MASE(Actual, Predicted):
|
5 |
+
'''
|
6 |
+
Mean Absolute Scaled Error (MASE)
|
7 |
+
'''
|
8 |
+
values = []
|
9 |
+
for i in range(1, len(Actual)):
|
10 |
+
values.append(abs(Actual[i] - Predicted[i]) /
|
11 |
+
(abs(Actual[i] - Actual[i - 1]) / (len(Actual) - 1)))
|
12 |
+
|
13 |
+
return numpy.mean(values)
|
src/functions/order_qty_rmse.py
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
from sklearn.metrics import mean_squared_error
|
3 |
+
import numpy as np
|
4 |
+
|
5 |
+
|
6 |
+
def order_qty_rmse(actual, predicted):
|
7 |
+
actu = []
|
8 |
+
pred = []
|
9 |
+
for i, a in enumerate(actual):
|
10 |
+
if not a == 0:
|
11 |
+
actu.append(actual[i])
|
12 |
+
pred.append(predicted[i])
|
13 |
+
return np.sqrt(mean_squared_error(actu, pred))
|
src/functions/sort_res.py
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
def sort_res_by_rmse(res):
|
2 |
+
pass
|
3 |
+
|
4 |
+
|
5 |
+
def sort_res_by_(res):
|
6 |
+
pass
|