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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
NuPIC is a machine learning framework that implements Hierarchical Temporal Memory (HTM) theory, a neuroscience-inspired approach to artificial intelligence. It models principles of the neocortex to build systems capable of learning patterns from streaming data, performing sequence prediction, and detecting anomalies in real-time data streams. The framework is built around a Cortical Learning Algorithm that combines spatial pooling and temporal memory to process streaming input. It uses Sparse Distributed Representations to encode input patterns, a Spatial Pooler to convert dense input into s
sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran
DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f
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 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.
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…