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Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate and multivariate temporal data. It serves as a comprehensive framework for training and evaluating a wide range of statistical, machine learning, and deep learning models to predict future numerical values. The toolkit is distinguished by its support for global time series modeling, allowing a single model to be trained across multiple different series to leverage shared patterns. It also features a hierarchical time series manager to ensure consistency between aggregate and
Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It enables the prediction of future values across diverse datasets without requiring task-specific training or optimization. The system functions as a probabilistic forecasting tool, producing multiple future trajectories and quantile forecasts to quantify uncertainty and potential prediction errors. It incorporates exogenous covariate integration to merge external variables and historical context into the input stream for increased precision. The project includes utilities for sy
This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It implements specialized architectures for time series forecasting, anomaly detection, data imputation, and classification. The project distinguishes itself through the inclusion of zero-shot inference capabilities, allowing large-scale temporal models to be evaluated on unseen datasets without requiring task-specific fine-tuning. The framework covers a broad range of analytical capabilities, including the recovery of missing values in incomplete datasets, the identification of irregul
AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc
TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and anomaly detection. It functions as a pretrained model for predicting future values in univariate time series data, eliminating the need for manual training from scratch.
The main features of google-research/timesfm are: Time Series Forecasting, Cross-Dataset Pretraining, Time Series Anomaly Detection, Zero-Shot Forecasting, Time Series Forecasting Models, Low-Rank Adaptation, Temporal Patching, Dynamic Covariate Integration.
Projects with overlapping indexed features include: unit8co/darts — Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate… amazon-science/chronos-forecasting — Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It… thuml/time-series-library — This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It… autogluon/autogluon — AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end… numenta/nupic — NuPIC is a machine learning framework that implements Hierarchical Temporal Memory (HTM) theory, a… nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts…