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.
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.
Kats supports functionalities to detect various patterns on time series data, including seasonalities, outlier, change point, and slow trend changes.
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.
Kats also provides a set of useful utilities, such as time series simulators.