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Datasets:
Salesforce
/
GiftEvalPretrain

Tasks:
Time Series Forecasting
Modalities:
Time-series
Size:
1M<n<10M
ArXiv:
Tags:
timeseries
forecasting
benchmark
gifteval
License:
Dataset card Viewer Files Community
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
list<item: float>
to
Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=2, id=None)
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/src/worker/job_runners/config/parquet_and_info.py", line 686, in wrapped
                  for item in generator(*args, **kwargs):
                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 2245, in cast_table_to_schema
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
                  cast_array_to_feature(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              list<item: float>
              to
              Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), length=2, id=None)
              
              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 1412, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 988, in stream_convert_to_parquet
                  builder._prepare_split(
                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
past_feat_dynamic_real
sequence
0
2016-02-29T05:00:00
30T
[[55.0,123.0,207.0,341.0,526.0,709.0,1196.0,1595.0,1854.0,2558.0,2756.0,2528.0,2643.0,2203.0,1627.0,(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
1
2016-02-29T05:00:00
30T
[[26.0,43.0,58.0,132.0,207.0,323.0,370.0,617.0,991.0,1241.0,1723.0,1917.0,1625.0,1259.0,1027.0,691.0(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
2
2016-02-29T05:00:00
30T
[[18.0,35.0,68.0,118.0,254.0,393.0,542.0,741.0,906.0,1174.0,1248.0,1344.0,1248.0,1034.0,777.0,614.0,(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
3
2016-02-29T05:00:00
30T
[[12.0,46.0,80.0,77.0,159.0,245.0,468.0,880.0,1335.0,1614.0,1835.0,1980.0,1831.0,1472.0,1189.0,835.0(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
4
2016-02-29T05:00:00
30T
[[15.0,21.0,48.0,78.0,171.0,243.0,456.0,768.0,1124.0,1395.0,1761.0,2108.0,1996.0,1454.0,1209.0,805.0(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
5
2016-02-29T05:00:00
30T
[[10.0,20.0,34.0,78.0,90.0,179.0,328.0,482.0,738.0,854.0,1029.0,1204.0,1087.0,1037.0,720.0,630.0,471(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
6
2016-02-29T05:00:00
30T
[[6.0,12.0,22.0,43.0,59.0,141.0,198.0,332.0,515.0,601.0,776.0,844.0,681.0,582.0,473.0,439.0,304.0,28(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
7
2016-02-29T05:00:00
30T
[[2.0,10.0,8.0,16.0,34.0,47.0,87.0,121.0,178.0,221.0,326.0,427.0,396.0,354.0,244.0,200.0,166.0,164.0(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
8
2016-02-29T05:00:00
30T
[[7.0,15.0,19.0,21.0,45.0,65.0,119.0,190.0,245.0,292.0,505.0,517.0,501.0,445.0,391.0,273.0,221.0,176(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
9
2016-02-29T05:00:00
30T
[[2.0,4.0,20.0,21.0,37.0,53.0,117.0,153.0,223.0,335.0,451.0,586.0,507.0,450.0,344.0,268.0,236.0,169.(...TRUNCATED)
[[0.032260000705718994,0.032260000705718994,0.06452000141143799,0.06452000141143799,0.03226000070571(...TRUNCATED)
End of preview.

GIFT-Eval Pre-training Datasets

Pretraining dataset aligned with GIFT-Eval that has 71 univariate and 17 multivariate datasets, spanning seven domains and 13 frequencies, totaling 4.5 million time series and 230 billion data points. Notably this collection of data has no leakage issue with the train/test split and can be used to pretrain foundation models that can be fairly evaluated on GIFT-Eval.

📄 Paper

🖥️ Code

📔 Blog Post

🏎️ Leader Board

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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