Datasets:
Tasks:
Time Series Forecasting
Sub-tasks:
univariate-time-series-forecasting
Size:
1K<n<10K
License:
File size: 6,816 Bytes
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# 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
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