Meet Kats — a one-stop shop for time series analysis
By Xiaodong Jiang
What it is: 
A new library to analyze 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, which provides both classical and advanced techniques to model time series data.   
What it does: 
Kats provides a set of algorithms and models for four domains in time series analysis: forecasting, detection, feature extraction and embedding, and multivariate analysis.
  1. Forecasting: Kats provides a full set of tools for forecasting that includes 10+ individual forecasting models, ensembling, a self-supervised learning (meta-learning) model, backtesting, hyperparameter tuning, and empirical prediction intervals.
  2. Detection: Kats supports functionalities to detect various patterns on time series data, including seasonalities, outlier, change point, and slow trend changes.
  3. Feature extraction and embedding: 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. 
  4. Useful utilities: Kats also provides a set of useful utilities, such as time series simulators.
Why it matters: 
Time series analysis is a fundamental domain in 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 to develop the standards and connect various domains in time series analysis, where the users can explore the basic characteristics of their time series data, predict the future values, monitor the anomalies, and incorporate them into their ML models and pipelines. 
Get it on GitHub:
Network hose: Managing uncertain network demand with model simplicity
Consolidating Facebook storage infrastructure with Tectonic file system
Read More in Open SourceView All
SEP 2, 2021
CacheLib, Facebook’s open source caching engine for web-scale services
AUG 11, 2021
Open-sourcing a more precise time appliance
AUG 4, 2021
Open sourcing Winterfell: A STARK prover and verifier
JUL 15, 2021
Fully Sharded Data Parallel: faster AI training with fewer GPUs
APR 29, 2021
A brief history of Rust at Facebook
FEB 5, 2021
Open-sourcing Thrift for Haskell
Related Posts
Jun 15, 2021
Network hose: Managing uncertain network demand with model simplicity
Apr 29, 2021
A brief history of Rust at Facebook
Feb 05, 2021
Open-sourcing Thrift for Haskell
Related Positions
Software Engineer, Language
Software Engineer, Language
Front End Engineer
Software Engineer, Core ML
Software Engineer, Core ML
See All Jobs
Facebook © 2021
To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy
Facebook EngineeringOpen SourceFacebook Open SourceAndroidiOSWebCore DataData InfrastructureDevInfraProduction EngineeringSecurityConnectivityData Center EngineeringNetworking & TrafficVideo EngineeringVirtual RealityResearch PublicationsML ApplicationsAI ResearchResearch PublicationsWatch Videos