• API

Kats

One stop shop for time series analysis in Python

a toolKit to Analyze Time Series

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. Kats is released by Facebook's Infrastructure Data Science team. It is available for download on PyPI.

Forecasting

Kats provides a full set of tools for forecasting that includes 10+ individual forecasting models, ensembling, a selfsupervised learning (meta-learning) model, backtesting, hyperparameter tuning, and empirical prediction intervals.

Detection

Kats supports functionalities to detect various patterns on time series data, including seasonalities, outlier, change point, and slow trend changes

TSFeatures

The time series feature (TSFeature) extraction module in Kats can produce 65 features with clear statistical definitions, which can be incorporated in most machine learning (ML) models, such as classification and regression.

Multivariate

Multivariate time series have more than one time-dependent variable, where those time series might inter-correlated with each other. Learning such inter-dependencies can improve the performance of many time series analysis tasks including forecasting and detection. Kats currently provides multivariate models including VAR and AR-Net, we also support algorithm for multivariate anomaly detection.

Visualization

Coming soon...

Utilities

Kats also provides a set of useful utilities, such as time series simulators.

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