Datasets:
Tasks:
Time Series Forecasting
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
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File size: 9,744 Bytes
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# Copyright 2020 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 Transformer Temperature (ETT) dataset."""
from dataclasses import dataclass
import pandas as pd
import datasets
_CITATION = """\
@inproceedings{haoyietal-informer-2021,
author = {Haoyi Zhou and
Shanghang Zhang and
Jieqi Peng and
Shuai Zhang and
Jianxin Li and
Hui Xiong and
Wancai Zhang},
title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},
volume = {35},
number = {12},
pages = {11106--11115},
publisher = {{AAAI} Press},
year = {2021},
}
"""
_DESCRIPTION = """\
The data of Electricity Transformers from two separated counties
in China collected for two years at hourly and 15-min frequencies.
Each data point consists of the target value "oil temperature" and
6 power load features. The train/val/test is 12/4/4 months.
"""
_HOMEPAGE = "https://github.com/zhouhaoyi/ETDataset"
_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"h1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv",
"h2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv",
"m1": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm1.csv",
"m2": "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTm2.csv",
}
@dataclass
class ETTBuilderConfig(datasets.BuilderConfig):
"""ETT builder config."""
prediction_length: int = 24
multivariate: bool = False
class ETT(datasets.GeneratorBasedBuilder):
"""Electricity Transformer Temperature (ETT) dataset"""
VERSION = datasets.Version("1.0.0")
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('ett', 'h1')
# data = datasets.load_dataset('ett', 'm2')
BUILDER_CONFIGS = [
ETTBuilderConfig(
name="h1",
version=VERSION,
description="Time series from first county at hourly frequency.",
),
ETTBuilderConfig(
name="h2",
version=VERSION,
description="Time series from second county at hourly frequency.",
),
ETTBuilderConfig(
name="m1",
version=VERSION,
description="Time series from first county at 15-min frequency.",
),
ETTBuilderConfig(
name="m2",
version=VERSION,
description="Time series from second county at 15-min frequency.",
),
]
DEFAULT_CONFIG_NAME = "h1" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
if self.config.multivariate:
features = datasets.Features(
{
"start": datasets.Value("timestamp[s]"),
"target": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
"feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
"item_id": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"start": datasets.Value("timestamp[s]"),
"target": datasets.Sequence(datasets.Value("float32")),
"feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
"feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
"item_id": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
filepath = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath,
"split": "dev",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
data = pd.read_csv(filepath, parse_dates=True, index_col=0)
start_date = data.index.min()
if self.config.name in ["m1", "m2"]:
factor = 4 # 15-min frequency
else:
factor = 1 # hourly frequency
train_end_date_index = 12 * 30 * 24 * factor # 1 year
if split == "dev":
end_date_index = train_end_date_index + 4 * 30 * 24 * factor # 1 year + 4 months
else:
end_date_index = train_end_date_index + 8 * 30 * 24 * factor # 1 year + 8 months
if self.config.multivariate:
if split in ["test", "dev"]:
# rolling windows of prediction_length for dev and test
for i, index in enumerate(
range(
train_end_date_index,
end_date_index,
self.config.prediction_length,
)
):
yield i, {
"start": start_date,
"target": data[: index + self.config.prediction_length].values.astype("float32").T,
"feat_static_cat": [0],
"item_id": "0",
}
else:
yield 0, {
"start": start_date,
"target": data[:train_end_date_index].values.astype("float32").T,
"feat_static_cat": [0],
"item_id": "0",
}
else:
if split in ["test", "dev"]:
# rolling windows of prediction_length for dev and test
for i, index in enumerate(
range(
train_end_date_index,
end_date_index,
self.config.prediction_length,
)
):
target = data["OT"][: index + self.config.prediction_length].values.astype("float32")
feat_dynamic_real = data[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL"]][
: index + self.config.prediction_length
].values.T.astype("float32")
yield i, {
"start": start_date,
"target": target,
"feat_dynamic_real": feat_dynamic_real,
"feat_static_cat": [0],
"item_id": "OT",
}
else:
target = data["OT"][:train_end_date_index].values.astype("float32")
feat_dynamic_real = data[["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL"]][
:train_end_date_index
].values.T.astype("float32")
yield 0, {
"start": start_date,
"target": target,
"feat_dynamic_real": feat_dynamic_real,
"feat_static_cat": [0],
"item_id": "OT",
}
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