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Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple series using deep learning architectures. It functions as a distributed machine learning forecasting framework that enables the training of global models across multiple time series to improve generalization through cross-learning. The project distinguishes itself as a probabilistic forecasting toolkit that produces uncertainty intervals and probability distributions rather than single point estimates. It also includes a hierarchical forecast reconciler to ensure that predictions a
statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts and prediction intervals. It functions as a distributed time series framework that utilizes a C-based forecasting engine and an automated model selector to identify and fit the optimal statistical model for every unique series in a dataset. The system also includes a time series anomaly detector to identify unusual data points by comparing observed values against probabilistic forecast intervals. The project is distinguished by its ability to handle massive-scale parallel forec
GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n
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
[license-image]: https://img.shields.io/badge/License-MIT-blue.svg [license-url]: https://opensource.org/licenses/MIT [pypi-image]: https://badge.fury.io/py/tfts.svg [pypi-url]: https://pypi.python.org/pypi/tfts [pepy-image]: https://pepy.tech/badge/tfts/month [pepy-url]:…
The main features of longxingtan/time-series-prediction are: Time Series Analysis, Analysis Toolkits.
Projects with overlapping indexed features include: nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts… thuml/time-series-library — This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It… awslabs/gluonts — GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for… nixtla/neuralforecast — Neuralforecast is a neural time series forecasting library designed to predict future values for one or multiple… sktime/sktime — sktime is a machine learning framework for time series analysis. It provides a unified toolkit for implementing time… unit8co/darts — Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate…