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facebookresearch/Kats

0
View on GitHub↗
6,311 stars·622 forks·Python·MIT·25 views

Kats

Kats is a time series analysis framework and library providing tools for statistical characterization, anomaly detection, and trend forecasting. It functions as a toolkit for predicting future values based on historical data and identifying irregular patterns or structural change points within temporal sequences.

The project includes a temporal feature extraction tool to calculate descriptive statistics and characteristics that summarize time series behavior. It also provides a system for model hyperparameter tuning using self-supervised learning to improve the scale and generalization of predictions.

Features

  • Time Series Forecasting - Predicts future values in time series based on historical data using a variety of statistical and machine learning models.
  • Temporal Shift Detection - Detects both individual point anomalies and larger structural statistical shifts in temporal sequences.
  • Time Series Anomaly Detection - Identifies irregular patterns or significant shifts in temporal data using specialized detection algorithms.
  • Anomaly Detection - Identifies irregular patterns or significant shifts in data to flag outliers and structural breaks.
  • Anomaly Detection Algorithms - Provides a set of algorithms for identifying outliers and structural change points within temporal data sequences.
  • Time Series Analysis - Calculates statistics and characteristics of temporal data to understand key behaviors and trends.
  • Time Series Analysis Toolkits - Provides a comprehensive toolkit for statistical characterization, anomaly detection, and trend forecasting of temporal data.
  • Temporal Feature Extractors - Calculates descriptive statistics and characteristics to summarize the behavior of time series for predictive modeling.
  • Hyperparameter Tuning - Utilizes predefined search spaces and self-supervised metrics to automatically optimize model hyperparameters.
  • Descriptive Statistic Extractions - Calculates descriptive statistics and characteristics of temporal data to summarize behavior for downstream analysis.
  • Statistical Signal Decompositions - Breaks down complex temporal signals into trend and seasonality components to simplify analysis.
  • Time Series Analysis - Facebook's library for time series analysis and prediction.

Star history

Star history chart for facebookresearch/katsStar history chart for facebookresearch/kats

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does facebookresearch/kats do?

Kats is a time series analysis framework and library providing tools for statistical characterization, anomaly detection, and trend forecasting. It functions as a toolkit for predicting future values based on historical data and identifying irregular patterns or structural change points within temporal sequences.

What are the main features of facebookresearch/kats?

The main features of facebookresearch/kats are: Time Series Forecasting, Temporal Shift Detection, Time Series Anomaly Detection, Anomaly Detection, Anomaly Detection Algorithms, Time Series Analysis, Time Series Analysis Toolkits, Temporal Feature Extractors.

What are some open-source alternatives to facebookresearch/kats?

Open-source alternatives to facebookresearch/kats include: sktime/sktime — sktime is a machine learning framework for time series analysis. It provides a unified toolkit for implementing time… numenta/nupic — NuPIC is a machine learning framework that implements Hierarchical Temporal Memory (HTM) theory, a… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… hosseinmoein/dataframe — DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous… google-research/timesfm — TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and… nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts…