The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    NonStreamableDatasetError
Message:      Streaming is not possible for this dataset because data host server doesn't support HTTP range requests. You can still load this dataset in non-streaming mode by passing `streaming=False` (default)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 444, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 347, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 364, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 512, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 452, in open
                  out = open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 280, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__
                  self.zip = zipfile.ZipFile(
                File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
                  self._RealGetContents()
                File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
                  endrec = _EndRecData(fp)
                File "/usr/local/lib/python3.9/zipfile.py", line 263, in _EndRecData
                  fpin.seek(0, 2)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 759, in seek
                  raise ValueError("Cannot seek streaming HTTP file")
              ValueError: Cannot seek streaming HTTP file
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 126, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 87, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2567, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1382, in as_streaming_dataset
                  splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
                File "/tmp/modules-cache/datasets_modules/datasets/electricity_load_diagrams/fe3dd01c39428ad92523a7ced0df3fdf669cb0548b3dd16fb9f7009381aa440f/electricity_load_diagrams.py", line 109, in _split_generators
                  df = pd.read_csv(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 784, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 515, in xopen
                  raise NonStreamableDatasetError(
              datasets.download.streaming_download_manager.NonStreamableDatasetError: Streaming is not possible for this dataset because data host server doesn't support HTTP range requests. You can still load this dataset in non-streaming mode by passing `streaming=False` (default)

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Dataset Card for Electricity Load Diagrams

Dataset Summary

This dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.

Dataset Usage

The dataset has the following configuration parameters:

  • freq is the time series frequency at which we resample (default: "1H")
  • prediction_length is the forecast horizon for this task which is used to make the validation and test splits (default: 24)
  • rolling_evaluations is the number of rolling window time series in the test split for evaluation purposes (default: 7)

For example, you can specify your own configuration different from those used in the papers as follows:

load_dataset("electricity_load_diagrams", "uci", rolling_evaluations=10)

Notes:

  • Data set has no missing values.
  • Values are in kW of each 15 min rescaled to hourly. To convert values in kWh values must be divided by 4.
  • All time labels report to Portuguese hour, however all days present 96 measures (24*4).
  • Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points.
  • Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.

Supported Tasks and Leaderboards

  • univariate-time-series-forecasting: The time series forecasting tasks involves learning the future target values of time series in a dataset for the prediction_length time steps. The results of the forecasts can then be validated via the ground truth in the validation split and tested via the test split.

Languages

Dataset Structure

Data set has no missing values. The raw values are in kW of each 15 min interval and are resampled to hourly frequency. Each time series represent one client. Some clients were created after 2011. In these cases consumption were considered zero. All time labels report to Portuguese hour, however all days contain 96 measurements (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.

Data Instances

A sample from the training set is provided below:

{
  'start': datetime.datetime(2012, 1, 1, 0, 0),
  'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, 20.0, 20.0, 13.0, 11.0], # <= this target array is a concatenated sample
  'feat_static_cat': [0], 
  'item_id': '0'
}

We have two configurations uci and lstnet, which are specified as follows.

The time series are resampled to hourly frequency. We test on 7 rolling windows of prediction length of 24.

The uci validation therefore ends 24*7 time steps before the end of each time series. The training split ends 24 time steps before the end of the validation split.

For the lsnet configuration we split the training window so that it is 0.6-th of the full time series and the validation is 0.8-th of the full time series and the last 0.2-th length time windows is used as the test set of 7 rolling windows of the 24 time steps each. Finally, as in the LSTNet paper, we only consider time series that are active in the year 2012--2014, which leaves us with 320 time series.

Data Fields

For this univariate regular time series we have:

  • start: a datetime of the first entry of each time series in the dataset
  • target: an array[float32] of the actual target values
  • feat_static_cat: an array[uint64] which contains a categorical identifier of each time series in the dataset
  • item_id: a string identifier of each time series in a dataset for reference

Given the freq and the start datetime, we can assign a datetime to each entry in the target array.

Data Splits

name train unsupervised test
uci 370 2590 370
lstnet 320 2240 320

Dataset Creation

The Electricity Load Diagrams 2011–2014 Dataset was developed by Artur Trindade and shared in UCI Machine Learning Repository. This dataset covers the electricity load of 370 substations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min. We will resample this to hourly time series.

Curation Rationale

Research and development of load forecasting methods. In particular short-term electricity forecasting.

Source Data

This dataset covers the electricity load of 370 sub-stations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min.

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@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}
}

Contributions

Thanks to @kashif for adding this dataset.

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