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Datasets:
wxcai
/
California_ISO

Tasks:
Time Series Forecasting
Languages:
English
Size:
1B<n<10B
Tags:
TimeSeries,
Multimodal,
Heterogeneous
Dataset card Files Community
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
all: struct<len: int64, sensor_downtime: struct<0: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 1: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 2: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 3: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 4: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 5: struct<time: list<item: timestamp[s]>, index: list<item: int64>>>>
vs
all_except_battery: struct<len: int64, sensor_downtime: struct<0: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 1: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 2: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 3: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 4: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 5: struct<time: list<item: timestamp[s]>, index: list<item: int64>>>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 527, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              all: struct<len: int64, sensor_downtime: struct<0: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 1: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 2: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 3: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 4: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 5: struct<time: list<item: timestamp[s]>, index: list<item: int64>>>>
              vs
              all_except_battery: struct<len: int64, sensor_downtime: struct<0: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 1: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 2: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 3: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 4: struct<time: list<item: timestamp[s]>, index: list<item: int64>>, 5: struct<time: list<item: timestamp[s]>, index: list<item: int64>>>>

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WIATS: Weather-centric Intervention-Aware Time Series Multimodal Dataset

Data source:

Dataset Structure

The dataset is organized into the following structure:

|-- subdataset1

|   |-- raw_data  # Original data files
|   |-- time_series # Rule-based Imputed data files
|   |   |-- all_version_1.parquet # Time series data for each subject can be multivariate, can be in csv, parquet, etc.
|   |   |-- all_version_2.parquet
|   |   |-- ...
|   |   |-- id_info.json # Metadata for each subject

|   |-- weather
|   |   |-- location_1
|   |   |   |-- raw_data
|   |   |   |   |-- daily_weather_raw_????.json
|   |   |   |   |-- ...
|   |   |   |   |-- daily_weather_????.csv
|   |   |   |   |-- ...
|   |   |   |   |-- hourly_weather_????.csv
|   |   |   |   |-- ...
|   |   |   |-- weather_report (can be flattened and use regex to extract the version)
|   |   |   |   |-- version_1
|   |   |   |   |   |-- xxx_weather_report_????.json
|   |   |   |   |   |-- ...
|   |   |   |   |-- version_2
|   |   |   |   |-- ...
|   |   |   |-- report_embedding # embedding for the weather report
|   |   |   |   |-- version_1
|   |   |   |   |   |-- xxx_report_embedding_????.pkl
|   |   |   |   |   |-- ...
|   |   |   |   |-- version_2
|   |   |   |   |-- ...

|   |   |-- location_2
|   |   |-- ...

|   |   |-- merged_report_embedding # merged embedding for multiple needed locations (optional)
|   |   |   |-- xxx_embeddings_????.pkl
|   |   |   |-- ...

|   |   |-- merged_general_report # merged general report for multiple needed locations (optional)
|   |   |   |-- xxx_report.json
|   |   |   |-- ...

|   |-- scripts # Scripts for data processing, model training, and evaluation
|   |-- id_info.json # Metadata for whole dataset without preprocessing
|   |-- static_info.json # Static information for this dataset, including the dataset information, channel information, downtime reasons. 
|   |-- static_info_embeddings.pkl

|-- subdataset2
|-- ......

id_info.json Structure

The id_info.json file contains metadata for each subject in the dataset. Extracted from the raw dataset. The structure is as follows:

{
    "id_1": {
        "len": 1000, # Length of the time series data
        "sensor_downtime": {
            1: {
                "time": [yyyy-mm-dd hh:mm:ss, yyyy-mm-dd hh:mm:ss],
                "index": [start_index, end_index]
            },
            2: {
                "time": [yyyy-mm-dd hh:mm:ss, yyyy-mm-dd hh:mm:ss],
                "index": [start_index, end_index]
            },
            ...
        },
        "other_info_1": "value_1", # Other information about the subject customizable entry
        "other_info_2": "value_2",
        ...
    },
    "id_2": ...

}

static_info.json Structure

The static_info.json file contains static information for the whole dataset. The structure is as follows:

{
    "general_info": "description of the dataset",
    "downtime_prompt": "",
    "channel_info": {
        "id_1": {
            "channel_1": "channel 1 is xxx",
            "channel_2": "channel 2 is xxx"
        },
        "id_2": {
            "channel_1": "channel 1 is xxx",
            "channel_2": "channel 2 is xxx"
        },
        ...
    },
}
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