sktime/sktime
Sktime
sktime is a machine learning framework for time series analysis. It provides a unified toolkit for implementing time series classification, forecasting, and anomaly detection using standardized machine learning interfaces.
The library serves as a collection of tools for assigning categorical labels to temporal sequences, predicting future values based on historical patterns, and identifying outliers or unusual patterns within temporal data.
The framework includes capabilities for panel-data handling and pipeline-based transformations. It utilizes a unified API wrapper and plugin-based model integration to standardize diverse time series models, alongside reduction-based forecasting and window-based feature extraction.
Features
- Time Series Machine Learning - Provides a unified machine learning framework for the analysis, classification, and forecasting of time series data.
- Time Series Machine Learning Frameworks - A unified toolkit for implementing time series classification, forecasting, and anomaly detection using standard machine learning interfaces.
- Anomaly Detection - Implements algorithms and tools for identifying outliers or unusual patterns in temporal datasets.
- Time Series Anomaly Detection - Identifies unusual patterns or outliers within temporal data to signal unexpected events or system failures.
- Time Series Classification - Provides algorithms and frameworks for assigning categorical labels to temporal data based on shape and pattern.
- Time Series Forecasting - Implements models and architectures designed for predicting future values in temporal data sequences.
- Time Series ML Toolkits - Offers a comprehensive set of tools for applying machine learning to time series classification and analysis.
- Time Series Analysis - Provides analytical methods to assign categorical labels to temporal sequences based on underlying patterns.
- Window-Based - Generates statistical summaries from sliding time intervals to transform raw sequences into fixed-length input vectors.
- Reduction-Based Forecasting - Converts time series forecasting tasks into standard regression problems by flattening temporal data into feature vectors.
- Compositional Transformation Pipelines - Implements architectural patterns for chaining sequential data transformations and model estimators into executable pipelines.
- Model Adapter Plugins - Allows external machine learning libraries to be integrated via adapter classes that map foreign APIs to a common standard.
- Unified Model Wrappers - Standardizes diverse time series models under a consistent Scikit-Learn compatible interface for fitting and predicting.
- Time Series Analysis - Unified framework for machine learning with time series data.
- Time Series Analysis - Unified framework for machine learning with time series.
- Time Series Annotation - Framework for machine learning tasks involving time series data.
- Analysis Toolkits - Unified machine learning framework for diverse tasks.
