ts-eval 0.2.3
Time Series analysis and evaluation tools

# ```ts-eval``` Time Series analysis and evaluation tools

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---
A set of tools to make time series analysis easier.

## 🧩 Current features

* **N-step ahead rolling origin time series evaluation** – using a Jupyter widget.
* **Friedman / Nemenyi rank test (posthoc)** – to see which model statistically performs better.
* **Relative Metrics** – rMSE, rMAE + Forecasted Value analogues.
* **Prediction Interval Metrics** – MIS, rMIS, FVrMIS
* **Fixed fourier series generation**  – fixed in time according to pandas index
* **Naive/Seasonal models for baseline predictions** (with prediction intervals)
* **Statsmodels n-step evaluation** – helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.

## 👩🏾‍🎨 Widget Preview

In:
```
TSMetrics(target, sm_seas, default)
.use_reference(snaive)
.for_horizons(0, 1, 5, 23)
.for_time_slices(time_slices.all, time_slices.weekend)
.with_description()
.with_prediction_rankings(mtx.FVrMSE, mtx.FVrMIS)
.with_predictions_plot()
.show()
```

Out:
![Demo Screenshot](images/demo_screenshot.png)

## 👩🏾‍🚒 Demo
For a more elaborate example, please check out the [Demo Notebook](https://nbviewer.jupyter.org/github/vshulyak/ts-eval/blob/master/examples/basic_usage.ipynb).

Alternatively, check out [interactive Binder demo](https://mybinder.org/v2/gh/vshulyak/ts-eval/master?filepath=examples%2Fbasic_usage.ipynb)

## 🤦🏾‍ Motivation

While working on a long term time series analysis project, I had a need to summarize and store performance metrics
of different models and compare them. As it's daunting to do this across dozens of notebooks, I huddled over some code
to do what I want in a few lines of code.

## 👩🏾‍🚀 Installation

      pip install ts-eval


## 📋 Release Planning:

* **Release 0.3**
  * use pandas better for dataframe styles / viz https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html
  * api like (viz1 | viz2 | viz3 ) / viz4 (patchwork R package)
  * CRPS evaluation
  * dynamic insample forecast for statsmodels
  * PI coverage estimation is really needed (in %)
  * predictions based on loess decomposition https://github.com/jrmontag/STLDecompose
  * dataset description: first index - last index sample (dates or ids)
  * transform with callback
  * altair-like API where you can combine components with +
  * success rate of prediction intervals like here http://freerangestats.info/blog/2016/01/30/hybrid-forecasts
  * describe case when MIS rankings are better for one dataset, but its mean is worse (due to huge outliers)
  * wrapper around xarray datasets, which always returns non-NaN data and/or statistics that I need are computed
    inside of this wrapper. NaNs are always inside. Doable?
  * boxplots by timestep visualization (with boxplot, outliers for each step)
  * remove collection of deps in style [tests_and_bla_bla] to [tests,bla]
  * links to papers – AvgRelMAE (Davydenko and Fildes, 2013); link to Nemenyi paper / implementations
  * make graphs with PIs more narrow on 0,1,.. steps as there's too much space left (with an option to turn this off).
  * better API for the end user – minimize interaction with xarray
  * pep517 build / wheels / better setup.py as per Hynek
  * travis: add 3.8 default python when it's available
  * docs: supported metrics & API options
  * Maybe use api like Summary in statsmodels MLEModel class, it has extend methods and warn/info messages
  * pretty legend for lots like here https://studywolf.wordpress.com/2017/11/21/matplotlib-legends-for-mean-and-confidence-interval-plots/
  * Look for TODOs
  * changable colors
  * turn off colored display option
  * a nicer API for raw metrics container
  * codacy badge
  * boxplots to compare models (as an alternative)
  * violin plots to compare predictions – areas can be colored, different metrics on left and right (like relative...)
* **Release 0.4**
  * holiday/fourier features model
  * fix viz module to have less of important stuff
  * a gif with project visualization
  * check shapes of input arrays (target vs preds), now it doesn't raise an error
  * Baseline prediction using target dataset (without explicit calculation, but losing some time points)

## 💡 Ideas

* components
  * Graph: Visualize outliers from confidence interval
  * Multi-comparison component: scikit_posthocs lib or homecooked?
  * inspect true confidence interval coverage via sampling (was done in postings around bayesian dropout sampling)
  * xarrays: compare if compared datasets are actually equal (offets by dates, shapes, maybe even hashing)
  * bin together step performance, like steps 0-1, 2-5, 6-12, 13-24
  * highlight regions using a mask (holidays, etc.)
  * option to view interactively points using widget (plotly)?
  * diagnostics: bias to over / underestimate points
  * animated graphs for change in seasonality
* features
  * example notebook for fourier?
  * tests for fourier
  * nint generation
* utils:
  * model adaptor (for different models, generic) which generates 3d prediction dataset. For stastmodels using dyn forecast or kalman filter
  * future importance calculator, but only if I can manipulate input features
  * feature selection using PACF / prewhiten?
* project
  * more defensive style (add arg checks, so it's easier to understand what is going on)
  * docstrings
  * circleci
  * https://timothycrosley.github.io/portray/ for docs
* sMAPE & MASE can be added for the jupyter evaluation tables
* ? Residual stats: since I have residuals => Ljung-Box, Heteroscedasticity test, Jarque-Bera – like in statsmodels results,
  but probably these stats were inspected already by the user... and on which step should they be computed then?

## See also

* https://arch.readthedocs.io/en/latest/multiple-comparison/multiple-comparison_examples.html
* RMC as an anlogue for nemenyi https://github.com/config-i1/greybox/blob/master/R/rmc.R

## 🤹🏼‍♂️ Development

Recommended development workflow:
```
pipenv install -e .[dev]
pipenv shell
```
The library doesn't use Flit/Poetry, so the suggested workflow is based on Pipenv (as per https://github.com/pypa/pipenv/issues/1911).
Pipfile* are ignored in the .gitignore.
# Changelog

##  0.2.3 (2021-06-03)

### Fixes (unreleased changes from 2019, doh)

- Fix results for 1 time series (noop)


##  0.2.2 (2019-10-22)

### Fixes

- Fix nan values propagated to Friedman Nemenyi test.
- Critical distance is returned alongside Friedman Nemenyi test.


##  0.2.1 (2019-10-18)

### Fixes

Outdated import in wheel version of the package.


##  0.2.0 (2019-10-16)

### Features

- Multiple prediction ranking with Friedman Nemenyi posthoc.
- Visualization of prediction intervals
- Indication of prediction ranking in a colorful table
- Rewrite of the internal computation machinery


##  0.1.0 (2019-10-04)

### Features

- N-step ahead evaluation widget for Jupyter
- Absolute & relative metrics for point forecasts and prediction intervals (MSE, MAE, rMSE, rMAE, MIS, rMIS)
- Naive/Seasonal models for baseline (with prediction intervals)
- Helper functions to evaluate n-step ahead forecasts using Statsmodels models or naive/seasonal naive models.
- Holiday features generation and model evaluation on holiday datetimes.


## 0.0.1 (2019-09-18)

### Features

- Fixed fourier series generation (fixed in time according to pandas index)