# facebookresearch/kats

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6,311 stars · 622 forks · Python · MIT

## Links

- GitHub: https://github.com/facebookresearch/Kats
- awesome-repositories: https://awesome-repositories.com/repository/facebookresearch-kats.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Time Series Forecasting](https://awesome-repositories.com/f/artificial-intelligence-ml/time-series-forecasting.md) — Predicts future values in time series based on historical data using a variety of statistical and machine learning models. ([source](https://github.com/facebookresearch/kats#readme))
- [Temporal Shift Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/anomaly-detection/temporal-shift-detection.md) — Detects both individual point anomalies and larger structural statistical shifts in temporal sequences. ([source](https://facebookresearch.github.io/Kats/](https://facebookresearch.github.io/Kats/))
- [Time Series Anomaly Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/time-series-anomaly-detection.md) — Identifies irregular patterns or significant shifts in temporal data using specialized detection algorithms. ([source](https://github.com/facebookresearch/kats#readme))
- [Temporal Feature Extractors](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction/temporal-feature-extractors.md) — Calculates descriptive statistics and characteristics to summarize the behavior of time series for predictive modeling.
- [Hyperparameter Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/model-fine-tuning-resources/hyperparameter-tuning.md) — Utilizes predefined search spaces and self-supervised metrics to automatically optimize model hyperparameters.

### Data & Databases

- [Anomaly Detection](https://awesome-repositories.com/f/data-databases/anomaly-detection.md) — Identifies irregular patterns or significant shifts in data to flag outliers and structural breaks.
- [Anomaly Detection Algorithms](https://awesome-repositories.com/f/data-databases/anomaly-detection-algorithms.md) — Provides a set of algorithms for identifying outliers and structural change points within temporal data sequences.
- [Time Series Analysis](https://awesome-repositories.com/f/data-databases/time-series-analysis.md) — Calculates statistics and characteristics of temporal data to understand key behaviors and trends.
- [Time Series Analysis Toolkits](https://awesome-repositories.com/f/data-databases/time-series-analysis-tools/time-series-analysis-toolkits.md) — Provides a comprehensive toolkit for statistical characterization, anomaly detection, and trend forecasting of temporal data.
- [Descriptive Statistic Extractions](https://awesome-repositories.com/f/data-databases/time-series-analysis/descriptive-statistic-extractions.md) — Calculates descriptive statistics and characteristics of temporal data to summarize behavior for downstream analysis. ([source](https://github.com/facebookresearch/kats#readme))

### Scientific & Mathematical Computing

- [Statistical Signal Decompositions](https://awesome-repositories.com/f/scientific-mathematical-computing/wavelet-signal-decompositions/statistical-signal-decompositions.md) — Breaks down complex temporal signals into trend and seasonality components to simplify analysis.

### Part of an Awesome List

- [Time Series Analysis](https://awesome-repositories.com/f/awesome-lists/data/time-series-analysis.md) — Toolkit for analyzing time series data.