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Modeling Trend and Seasonality in Contrastive Learning for Time-Series Forecasting

Self-supervised contrastive learning has recently shown promise for time-series representation learning, yet most existing methods treat sequences holistically and leave trend and seasonal components entangled, limiting their effectiveness for long-horizon multivariate forecasting. We study decompos...

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Autori principali: Cheng-Ru Chou, Yen-Ching Lu, Pei-Xuan Li, Hsun-Ping Hsieh
Natura: Artigo
Lingua:Inglês
Pubblicazione: MDPI AG 2026-03-01
Serie:Applied Sciences
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Accesso online:https://www.mdpi.com/2076-3417/16/5/2521
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