tsflex 0.4.1
Toolkit for flexible processing & feature extraction on time-series data
License: MIT
Requires Python: <3.13,>=3.8
Keywords: time-series, processing, feature-extraction, data-science, machine learning

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> _tsflex_ is a toolkit for _**flex**ible **t**ime **s**eries_ [processing](https://predict-idlab.github.io/tsflex/processing) & [feature extraction](https://predict-idlab.github.io/tsflex/features), that is efficient and makes few assumptions about sequence data.

#### Useful links

- [Paper](https://www.sciencedirect.com/science/article/pii/S2352711021001904)
- [Documentation](https://predict-idlab.github.io/tsflex/)
- [Example (machine learning) notebooks](https://github.com/predict-idlab/tsflex/tree/main/examples)

#### Installation

|                                                      | command                               |
| :--------------------------------------------------- | :------------------------------------ |
| [**pip**](https://pypi.org/project/tsflex/)          | `pip install tsflex`                  |
| [**conda**](https://anaconda.org/conda-forge/tsflex) | `conda install -c conda-forge tsflex` |

## Usage

_tsflex_ is built to be intuitive, so we encourage you to copy-paste this code and toy with some parameters!

### <a href="https://predict-idlab.github.io/tsflex/features/#getting-started">Feature extraction</a>

```python
import pandas as pd; import numpy as np; import scipy.stats as ss
from tsflex.features import MultipleFeatureDescriptors, FeatureCollection
from tsflex.utils.data import load_empatica_data

# 1. Load sequence-indexed data (in this case a time-index)
df_tmp, df_acc, df_ibi = load_empatica_data(['tmp', 'acc', 'ibi'])

# 2. Construct your feature extraction configuration
fc = FeatureCollection(
    MultipleFeatureDescriptors(
          functions=[np.min, np.mean, np.std, ss.skew, ss.kurtosis],
          series_names=["TMP", "ACC_x", "ACC_y", "IBI"],
          windows=["15min", "30min"],
          strides="15min",
    )
)

# 3. Extract features
fc.calculate(data=[df_tmp, df_acc, df_ibi], approve_sparsity=True)
```

Note that the feature extraction is performed on multivariate data with varying sample rates.
| signal | columns | sample rate |
|:-------|:-------|------------------:|
| df_tmp | ["TMP"]| 4Hz |
| df_acc | ["ACC_x", "ACC_y", "ACC_z" ]| 32Hz |
| df_ibi | ["IBI"]| irregularly sampled |

### <a href="https://predict-idlab.github.io/tsflex/processing/index.html#getting-started">Processing</a>

[Working example in our docs](https://predict-idlab.github.io/tsflex/processing/index.html#working-example)

## Why tsflex? ✨

- `Flexible`:
  - handles multivariate/multimodal time series
  - versatile function support
    => **integrates** with many packages for:
    - processing (e.g., [scipy.signal](https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html#time-series-filters))
    - feature extraction (e.g., [numpy](https://numpy.org/doc/stable/reference/routines.html), [scipy.stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html), [antropy](https://raphaelvallat.com/antropy/build/html/api.html), [nolds](https://cschoel.github.io/nolds/nolds.html#algorithms), [seglearn](https://dmbee.github.io/seglearn/feature_functions.html)¹, [tsfresh](https://tsfresh.readthedocs.io/en/latest/text/list_of_features.html)¹, [tsfel](https://tsfel.readthedocs.io/en/latest/descriptions/feature_list.html)¹)
  - feature extraction handles **multiple strides & window sizes**
- `Efficient`:<br>
  - view-based operations for processing & feature extraction => extremely **low memory peak** & **fast execution time**<br>
    - see: [feature extraction benchmark visualization](https://predict-idlab.github.io/tsflex/#benchmark)
- `Intuitive`:<br>
  - maintains the sequence-index of the data
  - feature extraction constructs interpretable output column names
  - intuitive API
- `Few assumptions` about the sequence data:
  - no assumptions about sampling rate
  - able to deal with multivariate asynchronous data<br>i.e. data with small time-offsets between the modalities
- `Advanced functionalities`:
  - apply [FeatureCollection.**reduce**](https://predict-idlab.github.io/tsflex/features/index.html#tsflex.features.FeatureCollection.reduce) after feature selection for faster inference
  - use **function execution time logging** to discover processing and feature extraction bottlenecks
  - embedded [SeriesPipeline](http://predict-idlab.github.io/tsflex/processing/#tsflex.processing.SeriesPipeline.serialize) & [FeatureCollection](https://predict-idlab.github.io/tsflex/features/index.html#tsflex.features.FeatureCollection.serialize) **serialization**
  - time series [**chunking**](https://predict-idlab.github.io/tsflex/chunking/index.html)

¹ These integrations are shown in [integration-example notebooks](https://github.com/predict-idlab/tsflex/tree/main/examples).

## Future work 🔨

- scikit-learn integration for both processing and feature extraction<br>
  **note**: is actively developed upon [sklearn integration](https://github.com/predict-idlab/tsflex/tree/sklearn_integration) branch.
- Support time series segmentation (exposing under the hood strided-rolling functionality) - [see this issue](https://github.com/predict-idlab/tsflex/issues/15)
- Support for multi-indexed dataframes

=> Also see the [enhancement issues](https://github.com/predict-idlab/tsflex/issues?q=is%3Aissue+is%3Aopen+label%3Aenhancement+)

## Contributing 👪

We are thrilled to see your contributions to further enhance `tsflex`.<br>
See [this guide](CONTRIBUTING.md) for more instructions on how to contribute.

## Referencing our package

If you use `tsflex` in a scientific publication, we would highly appreciate citing us as:

```bibtex
@article{vanderdonckt2021tsflex,
    author = {Van Der Donckt, Jonas and Van Der Donckt, Jeroen and Deprost, Emiel and Van Hoecke, Sofie},
    title = {tsflex: flexible time series processing \& feature extraction},
    journal = {SoftwareX},
    year = {2021},
    url = {https://github.com/predict-idlab/tsflex},
    publisher={Elsevier}
}
```

Link to the paper: https://www.sciencedirect.com/science/article/pii/S2352711021001904

---

<p align="center">
👤 <i>Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost</i>
</p>