auto-sktime

KO | EN

gitlite — search

#typescript #ai-agents #ai #dsh-plugin #deepseek-harness #open-source #claude-code #codex #cli #developer-tools #react #windows

★ 19 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# auto-sktime

Automatic creation of time series forecasts, regression and classification.

## Installation

For trouble shooting and detailed installation instructions, see the documentation.

```
Operating system: Linux
Python version: Python 3.8, 3.9, 3.10 and 3.11 (only 64 bit)
Package managers: pip
```

### pip

auto-sktime is available in pip. You can see all available wheels [here](https://test.pypi.org/project/auto-sktime).

```bash
pip install auto-sktime
```

or, with maximum dependencies,

```bash
pip install auto-sktime[all_extras]
```

## auto-sktime: Automated Time Series Forecasting

This section describes how to reproduce the results in the _auto-sktime_ paper. First, install _auto-sktime_ either via
`pip` or from source as described above.

Next, switch to the `scripts/benchmark` directory and use

```bash
python benchmark.py
```

to benchmark all available methods on all datasets. Alternatively, you can also only execute the benchmark for selected
methods and/or datasets. For example, `pmdarima` can be benchmarked on the very first dataset using
```bash
python benchmark.py --method pmdarima --end-index 1
```

Check
```bash
python benchmark.py --help
```
for detailed information how to configure the benchmark.

### Reproducing results
To ensure fair comparisons due to parallel computing, the benchmark can and should be limited a single CPU core using

```bash
taskset --cpu-list 0 python benchmark.py
```

The raw results are going to be stored in CSV files on disk. To create the visualizations, use
```bash
python evaluation.py
```
after finishing the complete benchmark.

## Remaining Useful Life Predictions (AutoRUL)

This section describes how to reproduce the results in the _AutoRUL_ paper. First, checkout the exact code that was used
to create the results. Therefore, you can use the tag [v0.1.0](https://github.com/Ennosigaeon/auto-sktime/tree/v0.1.0)

```bash
git checkout tags/v0.1.0 -b autorul
```

Next, switch to the `scripts` directory and use

```bash
python remaining_useful_lifetime.py <BENCHMARK>
```

to run a single benchmark data set. To view the available benchmarks and all configuration parameters run

```bash
python remaining_useful_lifetime.py --help
```

### Reproducing results

You can use the following commands to recreate the reported baseline results in the experiments of the paper.

```bash
python remaining_useful_lifetime.py <BENCHMARK> --runcount_limit 1 --timeout 3600 --multi_fidelity False --include baseline_lstm
python remaining_useful_lifetime.py <BENCHMARK> --runcount_limit 1 --timeout 3600 --multi_fidelity False --include baseline_cnn
python remaining_useful_lifetime.py <BENCHMARK> --runcount_limit 1 --timeout 3600 --multi_fidelity False --include baseline_transformer
python remaining_useful_lifetime.py <BENCHMARK> --runcount_limit 1 --timeout 7200 --multi_fidelity False --include baseline_rf
python remaining_useful_lifetime.py <BENCHMARK> --runcount_limit 200 --timeout 7200 --multi_fidelity False --ensemble_size 1 --include baseline_svm
```

with `<BENCHMARK>` being one of `{cmapss,cmapss_1,cmapss_2,cmapss_3,cmapss_4,femto_bearing,filtration,phm08,phme20}`.
For the _AutoRUL_ evaluation only the benchmark is provided and all remaining default configurations are used.

```bash
python remaining_useful_lifetime.py <BENCHMARK>
```

To reproduce the results from AutoCoevoRUL, checkout the [repository](https://github.com/Ennosigaeon/AutoCoevoRUL) from
Github and use the [autocoevorul.py](scripts/autocoevorul.py) file to either export the data sets or import the results.

## Note

This project has been set up using PyScaffold 4.2.1. For details and usage
information on PyScaffold see https://pyscaffold.org/.

## Building

To create a new release of `auto-sktime` you will have to install `build` and `twine`

```bash
pip install build twine
python -m build
```

auto-sktime

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README