A Transformer-based Framework for Multivariate Time Series Representation Learning, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and ...

Mar 10, 2023 · We will show how to use the Informer model for the multivariate probabilistic forecasting task, ie, predicting the distribution of a future vector of time- ...

Oct 6, 2020 · In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.

Mar 24, 2021 · Transformers can be applied for time series forecasting. See for example "Adversarial Sparse Transformer for Time Series Forecasting" by Wu et al.

Aug 2, 2023 · To understand how to apply a transformer to a time series model, we need to focus on three key parts of the transformer architecture: Embedding ...

Oct 28, 2021 · We convert multivariate TSF into a super-long sequence prediction problem that is solvable with recent improvements to the Transformer ...

Dec 30, 2023 · This paper focuses on reducing redundant information to elevate forecasting accuracy while optimizing runtime efficiency.

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series. - qingsongedu/time-series-transformers-review.

May 7, 2024 · We propose an efficient transformer design for multivariate time series prediction. Our approach integrates two key components: (i) a gated residual attention ...

Jul 10, 2024 · Transformers have achieved remarkable performance in multivariate time series(MTS) forecasting due to their capability to capture long-term ...