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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)…
The main features of white-link/unsupervisedscalablerepresentationlearningtimeseries are: Representation Learning, Time Series Analysis.
Open-source alternatives to white-link/unsupervisedscalablerepresentationlearningtimeseries include: yuezhihan/ts2vec — This repository contains the official implementation for the paper TS2Vec: Towards Universal Representation of Time… spro/practical-pytorch — Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… alexiosg/rmgarch — Multivariate GARCH Models. alexiosg/rugarch — Univariate GARCH models in R. albertoalmuinha/garchmodels — The Tidymodels Extension for GARCH models.
This repository contains the official implementation for the paper TS2Vec: Towards Universal Representation of Time Series (AAAI-22).
Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural networks. The project provides practical code for building sequence models and sequence-to-sequence architectures using the PyTorch framework. The repository covers the implementation of models for neural machine translation, character-level text generation, and text classification. It includes examples for transforming input sequences into output sequences for machine translation and synthesizing new text. The project also extends to sequence data prediction and time series analy
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
The Tidymodels Extension for GARCH models