Fidel-TS
Collection
Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting • 6 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
general_info: string
downtime_prompt: string
channel_info: struct<weather_large: struct<p (mbar): string, T (degC): string, Tpot (K): string, Tdew (degC): stri (... 371 chars omitted)
child 0, weather_large: struct<p (mbar): string, T (degC): string, Tpot (K): string, Tdew (degC): string, rh (%): string, VP (... 348 chars omitted)
child 0, p (mbar): string
child 1, T (degC): string
child 2, Tpot (K): string
child 3, Tdew (degC): string
child 4, rh (%): string
child 5, VPmax (mbar): string
child 6, VPact (mbar): string
child 7, VPdef (mbar): string
child 8, sh (g/kg): string
child 9, H2OC (mmol/mol): string
child 10, rho (g/m³): string
child 11, wv (m/s): string
child 12, max. wv (m/s): string
child 13, wd (deg): string
child 14, rain (mm): string
child 15, raining (s): string
child 16, SWDR (W/m²): string
child 17, PAR (μmol/m²/s): string
child 18, max. PAR (μmol/m²/s): string
child 19, Tlog (degC): string
child 20, CO2 (ppm): string
weather_large: struct<sensor_downtime: struct<>>
child 0, sensor_downtime: struct<>
to
{'weather_large': {'sensor_downtime': {}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
general_info: string
downtime_prompt: string
channel_info: struct<weather_large: struct<p (mbar): string, T (degC): string, Tpot (K): string, Tdew (degC): stri (... 371 chars omitted)
child 0, weather_large: struct<p (mbar): string, T (degC): string, Tpot (K): string, Tdew (degC): string, rh (%): string, VP (... 348 chars omitted)
child 0, p (mbar): string
child 1, T (degC): string
child 2, Tpot (K): string
child 3, Tdew (degC): string
child 4, rh (%): string
child 5, VPmax (mbar): string
child 6, VPact (mbar): string
child 7, VPdef (mbar): string
child 8, sh (g/kg): string
child 9, H2OC (mmol/mol): string
child 10, rho (g/m³): string
child 11, wv (m/s): string
child 12, max. wv (m/s): string
child 13, wd (deg): string
child 14, rain (mm): string
child 15, raining (s): string
child 16, SWDR (W/m²): string
child 17, PAR (μmol/m²/s): string
child 18, max. PAR (μmol/m²/s): string
child 19, Tlog (degC): string
child 20, CO2 (ppm): string
weather_large: struct<sensor_downtime: struct<>>
child 0, sensor_downtime: struct<>
to
{'weather_large': {'sensor_downtime': {}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
| | |-- id_1.parquet # Time series data for each subject can be multivariate, can be in csv, parquet, etc.
| | |-- id_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
| | | | | |-- weather_report_????.json
| | | | | |-- ...
| | | | |-- version_2
| | | | |-- ...
| | | |-- report_embedding # embedding for the weather report
| | | | |-- version_1
| | | | | |-- report_embedding_????.pkl
| | | | | |-- ...
| | | | |-- version_2
| | | | |-- ...
| | |-- location_2
| | |-- ...
| | |-- merged_report_embedding # merged embedding for multiple needed locations (optional)
| | | |-- version_1
| | | | |-- report_embedding_????.pkl
| | | | |-- ...
| | | |-- version_2
| | | |-- ...
| | |-- merged_general_report # merged general report for multiple needed locations (optional)
| | | |-- xxx.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
| |-- slim_data (optional)
| |-- full_data (optional) # intermediate data during the data processing
|-- 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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