Datasets:
Tasks:
Time Series Forecasting
Sub-tasks:
univariate-time-series-forecasting
Size:
1K<n<10K
License:
| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Electricity Load Diagrams 2011-2014 time series dataset.""" | |
| from pathlib import Path | |
| import pandas as pd | |
| import datasets | |
| from .utils import to_dict | |
| _CITATION = """\ | |
| @inproceedings{10.1145/3209978.3210006, | |
| author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao}, | |
| title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks}, | |
| year = {2018}, | |
| isbn = {9781450356572}, | |
| publisher = {Association for Computing Machinery}, | |
| address = {New York, NY, USA}, | |
| url = {https://doi.org/10.1145/3209978.3210006}, | |
| doi = {10.1145/3209978.3210006}, | |
| booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval}, | |
| pages = {95--104}, | |
| numpages = {10}, | |
| location = {Ann Arbor, MI, USA}, | |
| series = {SIGIR '18} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014. | |
| """ | |
| _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014" | |
| _LICENSE = "" | |
| _URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00321/LD2011_2014.txt.zip" | |
| class ElectricityLoadDiagramsConfig(datasets.BuilderConfig): | |
| """A builder config with some added meta data.""" | |
| freq: str = "1H" | |
| prediction_length: int = 24 | |
| rolling_evaluations: int = 7 | |
| class ElectricityLoadDiagrams(datasets.GeneratorBasedBuilder): | |
| """Hourly electricity consumption of 370 points/clients.""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| ElectricityLoadDiagramsConfig( | |
| name="uci", | |
| version=VERSION, | |
| description="Original UCI time series.", | |
| ), | |
| ElectricityLoadDiagramsConfig( | |
| name="lstnet", | |
| version=VERSION, | |
| description="Electricity time series preprocessed as in LSTNet paper.", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "lstnet" | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "start": datasets.Value("timestamp[s]"), | |
| "target": datasets.Sequence(datasets.Value("float32")), | |
| "feat_static_cat": datasets.Sequence(datasets.Value("uint64")), | |
| # "feat_static_real": datasets.Sequence(datasets.Value("float32")), | |
| # "feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))), | |
| # "feat_dynamic_cat": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))), | |
| "item_id": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| train_ts = [] | |
| val_ts = [] | |
| test_ts = [] | |
| df = pd.read_csv( | |
| Path(data_dir) / "LD2011_2014.txt", | |
| sep=";", | |
| index_col=0, | |
| parse_dates=True, | |
| decimal=",", | |
| ) | |
| df.sort_index(inplace=True) | |
| df = df.resample(self.config.freq).sum() | |
| unit = pd.tseries.frequencies.to_offset(self.config.freq).name | |
| if self.config.name == "uci": | |
| val_end_date = df.index.max() - pd.Timedelta( | |
| self.config.prediction_length * self.config.rolling_evaluations, unit | |
| ) | |
| train_end_date = val_end_date - pd.Timedelta(self.config.prediction_length, unit) | |
| else: | |
| # concate the time series to be from 2012 till 2014 | |
| df = df[(df.index.year >= 2012) & (df.index.year <= 2014)] | |
| # drop time series which are zero at the start | |
| df = df.T[df.iloc[0] > 0].T | |
| # tran/val/test split from LSTNet paper | |
| # validation ends at 8/10-th of the time series | |
| val_end_date = df.index[int(len(df) * (8 / 10)) - 1] | |
| # training ends at 6/10-th of the time series | |
| train_end_date = df.index[int(len(df) * (6 / 10)) - 1] | |
| for cat, (ts_id, ts) in enumerate(df.iteritems()): | |
| start_date = ts.ne(0).idxmax() | |
| sliced_ts = ts[start_date:train_end_date] | |
| train_ts.append( | |
| to_dict( | |
| target_values=sliced_ts.values, | |
| start=start_date, | |
| cat=[cat], | |
| item_id=ts_id, | |
| ) | |
| ) | |
| sliced_ts = ts[start_date:val_end_date] | |
| val_ts.append( | |
| to_dict( | |
| target_values=sliced_ts.values, | |
| start=start_date, | |
| cat=[cat], | |
| item_id=ts_id, | |
| ) | |
| ) | |
| for i in range(self.config.rolling_evaluations): | |
| for cat, (ts_id, ts) in enumerate(df.iteritems()): | |
| start_date = ts.ne(0).idxmax() | |
| test_end_date = val_end_date + pd.Timedelta(self.config.prediction_length * (i + 1), unit) | |
| sliced_ts = ts[start_date:test_end_date] | |
| test_ts.append( | |
| to_dict( | |
| target_values=sliced_ts.values, | |
| start=start_date, | |
| cat=[cat], | |
| item_id=ts_id, | |
| ) | |
| ) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "split": train_ts, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "split": test_ts, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "split": val_ts, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, split): | |
| for key, row in enumerate(split): | |
| yield key, row | |