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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'end_time', 'root_cause', 'start_time', 'affected'}) and 19 missing columns ({'adbr', 'bso3', 'amud', 'bfo2', 'cfo1', 'asin1', 'adfl', 'aimp', 'bso2', 'cso1', 'y', 'timestamp', 'arnd', 'ced1', 'bed1', 'bed2', 'bso1', 'bfo1', 'asin2'}).

This happened while the csv dataset builder was generating data using

hf://datasets/patrickfleith/controlled-anomalies-time-series-dataset/metadata.csv (at revision 50184f8fbc1afc19e5eeeb9fbd6b2d3da391ccb0)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              start_time: string
              end_time: string
              root_cause: string
              affected: string
              category: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 846
              to
              {'timestamp': Value(dtype='string', id=None), 'aimp': Value(dtype='float64', id=None), 'amud': Value(dtype='float64', id=None), 'arnd': Value(dtype='float64', id=None), 'asin1': Value(dtype='float64', id=None), 'asin2': Value(dtype='float64', id=None), 'adbr': Value(dtype='float64', id=None), 'adfl': Value(dtype='float64', id=None), 'bed1': Value(dtype='float64', id=None), 'bed2': Value(dtype='float64', id=None), 'bfo1': Value(dtype='float64', id=None), 'bfo2': Value(dtype='float64', id=None), 'bso1': Value(dtype='float64', id=None), 'bso2': Value(dtype='float64', id=None), 'bso3': Value(dtype='float64', id=None), 'ced1': Value(dtype='float64', id=None), 'cfo1': Value(dtype='float64', id=None), 'cso1': Value(dtype='float64', id=None), 'y': Value(dtype='float64', id=None), 'category': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'end_time', 'root_cause', 'start_time', 'affected'}) and 19 missing columns ({'adbr', 'bso3', 'amud', 'bfo2', 'cfo1', 'asin1', 'adfl', 'aimp', 'bso2', 'cso1', 'y', 'timestamp', 'arnd', 'ced1', 'bed1', 'bed2', 'bso1', 'bfo1', 'asin2'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/patrickfleith/controlled-anomalies-time-series-dataset/metadata.csv (at revision 50184f8fbc1afc19e5eeeb9fbd6b2d3da391ccb0)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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timestamp
string
aimp
float64
amud
float64
arnd
float64
asin1
float64
asin2
float64
adbr
float64
adfl
float64
bed1
float64
bed2
float64
bfo1
float64
bfo2
float64
bso1
float64
bso2
float64
bso3
float64
ced1
float64
cfo1
float64
cso1
float64
y
float64
category
float64
2023-01-01 00:00:00
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2023-01-01 00:00:01
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2023-01-01 00:00:02
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2023-01-01 00:00:03
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2023-01-01 00:00:04
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2023-01-01 00:00:05
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2023-01-01 00:00:06
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2023-01-01 00:00:07
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2023-01-01 00:00:08
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2023-01-01 00:00:09
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2023-01-01 00:00:10
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2023-01-01 00:00:11
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21.481525
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2023-01-01 00:00:12
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21.645007
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2023-01-01 00:00:13
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21.671344
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2023-01-01 00:00:14
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2023-01-01 00:00:15
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2023-01-01 00:00:16
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2023-01-01 00:00:17
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2023-01-01 00:00:18
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2023-01-01 00:00:19
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2023-01-01 00:00:20
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2023-01-01 00:00:24
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2023-01-01 00:00:26
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2023-01-01 00:00:30
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2023-01-01 00:00:31
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2023-01-01 00:00:39
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2023-01-01 00:00:40
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2023-01-01 00:00:41
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2023-01-01 00:00:42
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2023-01-01 00:00:43
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2023-01-01 00:00:44
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2023-01-01 00:00:47
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2023-01-01 00:00:48
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2023-01-01 00:00:49
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2023-01-01 00:00:51
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2023-01-01 00:00:52
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2023-01-01 00:00:53
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2023-01-01 00:00:54
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2023-01-01 00:00:55
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2023-01-01 00:00:56
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2023-01-01 00:01:00
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2023-01-01 00:01:01
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2023-01-01 00:01:02
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1
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2023-01-01 00:01:03
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2023-01-01 00:01:04
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1
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2023-01-01 00:01:05
0
1
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2023-01-01 00:01:06
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2023-01-01 00:01:07
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Dataset Card for Dataset Name

Dataset Description

Cite the dataset as:

Patrick Fleith. (2023). Controlled Anomalies Time Series (CATS) Dataset (Version 2) [Data set]. Solenix Engineering GmbH. https://doi.org/10.5281/zenodo.8338435

Dataset Summary

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

Supported Tasks and Leaderboards

Anomaly Detection in Multivariate Time Series

Dataset Structure

  • Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:

    • 4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.
    • 3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.
  • 10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.

  • 5 million timestamps. Sensors readings are at 1Hz sampling frequency.

    • 1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.
    • 4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).
  • 200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.

  • Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.

  • Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.

  • Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).

  • Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.

  • Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.

  • Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.

  • No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

Data Splits

  • The first 1 million points are nominal (no occurence of anomalies)
  • The next 4 million points include both nominal and anomalous segments.

Licensing Information

license: cc-by-4.0

Citation Information

Patrick Fleith. (2023). Controlled Anomalies Time Series (CATS) Dataset (Version 1) [Data set]. Solenix Engineering GmbH. https://doi.org/10.5281/zenodo.7646897

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