Datasets:
Formats:
parquet
Sub-tasks:
multi-class-classification
univariate-time-series-forecasting
tabular-multi-class-classification
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
timeseries
time-series
time-series-forecasting
tabular-regression
tabular-classification
univariate-time-series-forecasting
License:
Update README.md
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README.md
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## Dataset Creation
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### Curation Rationale
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### Source Data
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## Dataset Creation
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### Curation Rationale
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A dataset has to comply with the following hard requirements to be eligible for MUSES.
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1. Inherently unevenly spaced
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2. Time series minimum length of 2
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3. Known license
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4. Used in Literature
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5. Replicably described data collection and processing
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6. Dataset has practical relevance or is commonly used for evaluation
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7. Maximum of 100 M events
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8. Nominal classes or common class analogy
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9. Commonly used in unevenly spaced time or TPP forecasting
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Further, we aim to fulfull the following variety constraints by dataset composition.
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1. Coverage of many different domains
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2. Variety in dataset sizes
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3. Variety in sequence lengths
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4. Variety in problem difficulties
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5. Variety in problem complexity
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6. Variety in class balances
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See our paper for further information.
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### Source Data
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