InterpreTS

InterpreTS is a Python library designed for extracting meaningful and interpretable features from time series data to support the creation of interpretable and explainable predictive models.

Overview

With the growing importance of interpretability in machine learning and AI, InterpreTS focuses on creating feature representations that facilitate the development of interpretable and explainable predictive models.

Key Features

• Statistical Features: Extract basic statistics like mean, standard deviation, minimum, and maximum values.
• Frequency Features: Calculate features in the frequency domain, such as Fourier Transform coefficients.
• Relational Features: Generate features describing relationships between different time series, such as correlation.
• Parallel Computing Support: Efficiently compute features with parallel processing.
• Data Format Flexibility: Easily convert and process data in pandas.DataFrame or numpy.array formats.

Requirements

• Python 3.8 or above
• pandas
• numpy
• statsmodels

Installation Guide

Follow these steps to install InterpreTS and its dependencies:

1. Clone the Repository
   Clone the InterpreTS repository to your local machine:

git clone https://github.com/ruleminer/InterpreTS.git
cd InterpreTS

2. Install dependencies: Install the required packages listed in the requirements.txt file:

pip install -r requirements.txt

3. Install InterpreTS: Run the following command to install InterpreTS:

pip install interpreTS

Verifying Installation

Once installed, you can verify the installation by running a simple feature extraction example:

from interpreTS.core.feature_extractor import FeatureExtractor, Features
import pandas as pd

# Sample time series data
data = pd.DataFrame({'value': [1, 2, 3, 4, 5]})
extractor = FeatureExtractor(features=[Features.LENGTH, Features.MEAN, Features.VARIANCE])
features = extractor.extract_features(data)
print("Extracted Features:\n", features)

Additional Usage Example with Time Series Data

You can also use InterpreTS with time-indexed data:

from interpreTS.core.time_series_data import TimeSeriesData
from interpreTS.core.feature_extractor import FeatureExtractor, Features
import pandas as pd

# Time-indexed data
data_with_date = pd.Series(
    [5, 3, 6, 2, 7, 4, 8, 3, 9, 1],
    index=pd.date_range("2023-01-01", periods=10, freq="D")
)
ts_data = TimeSeriesData(data_with_date)

# Feature extraction
extractor = FeatureExtractor(features=[Features.LENGTH, Features.MEAN, Features.VARIANCE])
features = extractor.extract_features(ts_data.data)
print("\nExtracted Features from Time Series Data:\n", features)

Documentation

Complete documentation is available on GitHub Pages.

Issues and Support

For any issues, please consult our Issue Tracker on GitHub.