Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model

Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model

Transformer-based time series forecasting models use patch tokens for temporal patterns and variate tokens to learn covariates’ dependencies. While patch tokens inherently facilitate self-supervised learning, variate tokens are more suitable for linear forecasters as they help to mitigate distributi...

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Bibliographic Details
Main Authors: Le Hoang Anh, Dang Thanh Vu, Seungmin Oh, Gwang-Hyun Yu, Nguyen Bui Ngoc Han, Hyoung-Gook Kim, Jin-Sul Kim, Jin-Young Kim
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
Subjects:
multivariate time series forecasting
transfer learning
frequency analysis
Online Access:https://www.mdpi.com/1996-1073/17/24/6452
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