What is it:
A new library for analyzing time series data. Kats is a lightweight, easy-to-use, and generalizable framework for generic time series analysis, including forecasting, anomaly detection, multivariate analysis, and feature extraction / embedding. To the best of our knowledge, Kats is the first comprehensive Python library for generic time series analysis that offers both classical and advanced techniques for modeling time series data.
What it does:
Kats offers a range of algorithms and models for four areas of time series analysis: prediction, detection, feature extraction and embedding, and multivariate analysis.
- forecast: Kats offers a complete set of prediction tools that include 10+ individual predictive models, ensembles, a self-monitored learning model (meta-learning), backtesting, hyperparameter tuning, and empirical prediction intervals.
- recognition: Kats supports features to detect various patterns in time series data including seasonality, outliers, point of change, and slow trend changes.
- Feature extraction and embedding: The Time Series Feature Extraction Module (TSFeature) in Kats can generate 65 features with clear statistical definitions that can be incorporated into most machine learning (ML) models such as classification and regression.
- Useful utilities: Kats also offers a number of useful utilities such as: B. Time series simulators.
Why it matters:
Time series analysis is a fundamental domain of data science and machine learning with massive applications in various sectors such as e-commerce, finance, capacity planning, supply chain management, medicine, weather, energy, astronomy and many others. Kats is the first comprehensive Python library that develops the standards and connects different domains in time series analysis, where users can examine the basic properties of their time series data, predict the future values, monitor the anomalies and incorporate them into their ML models, and piping .
Get it on GitHub:
Kats
Comments are closed.