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This repository contains the official implementation for the paper TS2Vec: Towards Universal Representation of Time Series (AAAI-22).
The main features of yuezhihan/ts2vec are: Representation Learning, Time Series Foundation Models, Time Series Analysis.
Open-source alternatives to yuezhihan/ts2vec include: huckiyang/voice2series-reprogramming — Voice2Series: Reprogramming / Prompting Acoustic Models for Time Series Classification. white-link/unsupervisedscalablerepresentationlearningtimeseries — This is the code corresponding to the experiments conducted for the work "Unsupervised Scalable Representation… amazon-science/chronos-forecasting — Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It… google-research/timesfm — TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and… time-series-foundation-models/lag-llama — Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates… spro/practical-pytorch — Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural…
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 project includes a framework for adapting pretrained weights to specific datasets using low-rank adaptation to improve accuracy. It also provides specialized capabilities for integrating time-series predictions as tools within autonomous AI agent architectures and automated workflows. The system supports
Voice2Series: Reprogramming / Prompting Acoustic Models for Time Series Classification
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 is the code corresponding to the experiments conducted for the work "Unsupervised Scalable Representation Learning for Multivariate Time Series" (Jean-Yves Franceschi, Aymeric Dieuleveut and Martin Jaggi)…