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
Browse files
README.md
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@@ -6582,13 +6582,13 @@ All the columns and classes (when textual) in MUSES are in English (BCP-47 `en`)
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All datasets formatting follows a similar structure.
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```
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{
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}
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```
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- `seq_idx`: an `int64` feature uniquely identifying sequences in the split.
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- `dim_process`: an `int64` feature representing total number of classes in the dataset.
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- `seq_len`: an `int64` feature containing sequence length.
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- `datetime`: (
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The classification labels differ for each dataset.
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The corresponding labels for each event type can be read from metadata like this
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All datasets formatting follows a similar structure.
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```
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{
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"datetime": [2020-06-10T14:27:37.963000000, 2020-06-20T21:23:06.980000000, 2020-10-15T13:36:16.687000000],
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"type_event": [7, 2, 15],
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"time_since_last_event": [0.0, 246.92472694444444, 2800.2193630555557],
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"time_since_start": [0.0, 246.92472694444444, 3047.14409],
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"seq_idx": 1,
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"dim_process": 50,
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"seq_len": 3
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}
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```
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- `seq_idx`: an `int64` feature uniquely identifying sequences in the split.
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- `dim_process`: an `int64` feature representing total number of classes in the dataset.
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- `seq_len`: an `int64` feature containing sequence length.
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- `datetime`: (if avail.) a `timestamp[ns]` sequence, containing event datetime if available.
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The classification labels differ for each dataset.
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The corresponding labels for each event type can be read from metadata like this
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