Datasets:

Salesforce
/

GiftEval

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
item_id: string
start: timestamp[s]
freq: string
target: list<item: float>
  child 0, item: float
past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7]
  child 0, item: list<item: float>
      child 0, item: float
-- schema metadata --
huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347
to
{'item_id': Value(dtype='string', id=None), 'start': Value(dtype='timestamp[s]', id=None), 'freq': Value(dtype='string', id=None), 'target': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 76, in _generate_tables
                  yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 59, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              item_id: string
              start: timestamp[s]
              freq: string
              target: list<item: float>
                child 0, item: float
              past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7]
                child 0, item: list<item: float>
                    child 0, item: float
              -- schema metadata --
              huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347
              to
              {'item_id': Value(dtype='string', id=None), 'start': Value(dtype='timestamp[s]', id=None), 'freq': Value(dtype='string', id=None), 'target': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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item_id string	start timestamp[s]	freq string	target sequence
0	2015-01-01T00:00:00	5T	[61.93913650512695,59.23252487182617,61.99180221557617,62.480655670166016,62.490482330322266,62.5417(...TRUNCATED)
1	2015-01-01T00:00:00	5T	[64.2808837890625,65.08245086669922,65.30912017822266,65.191650390625,65.28766632080078,68.001235961(...TRUNCATED)
2	2015-01-01T00:00:00	5T	[62.077396392822266,64.80834197998047,64.80391693115234,67.20659637451172,67.32328796386719,66.41279(...TRUNCATED)
3	2015-01-01T00:00:00	5T	[60.78642272949219,65.85395050048828,64.26608276367188,63.988426208496094,64.70740509033203,62.72486(...TRUNCATED)
4	2015-01-01T00:00:00	5T	[63.12067413330078,59.20623016357422,62.239200592041016,65.80850982666016,65.70866394042969,63.67469(...TRUNCATED)
5	2015-01-01T00:00:00	5T	[64.44831848144531,62.4967155456543,63.816612243652344,64.75755310058594,65.35836791992188,63.123970(...TRUNCATED)
6	2015-01-01T00:00:00	5T	[63.41112518310547,65.99217987060547,60.19683074951172,62.01144790649414,65.09144592285156,62.115444(...TRUNCATED)
7	2015-01-01T00:00:00	5T	[64.7394790649414,64.71804809570312,65.44779205322266,66.33447265625,63.09504699707031,68.0616760253(...TRUNCATED)
8	2015-01-01T00:00:00	5T	[63.009918212890625,61.24407196044922,63.79776382446289,61.702735900878906,62.18679428100586,61.0574(...TRUNCATED)
9	2015-01-01T00:00:00	5T	[65.26490020751953,65.60872650146484,66.01715850830078,65.73542785644531,65.09737396240234,65.096916(...TRUNCATED)

End of preview.

GIFT-Eval

We present GIFT-Eval, a benchmark designed to advance zero-shot time series forecasting by facilitating evaluation across diverse datasets. GIFT-Eval includes 23 datasets covering 144,000 time series and 177 million data points, with data spanning seven domains, 10 frequencies, and a range of forecast lengths. This benchmark aims to set a new standard, guiding future innovations in time series foundation models.

To facilitate the effective pretraining and evaluation of foundation models, we also provide a non-leaking pretraining dataset --> GiftEvalPretrain.

Submitting your results

If you want to submit your own results to our leaderborad please follow the instructions detailed in our github repository

Citation

If you find this benchmark useful, please consider citing:

@article{aksu2024giftevalbenchmarkgeneraltime,
      title={GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation}, 
      author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo},
      journal = {arxiv preprint arxiv:2410.10393},
      year={2024},
}

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