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cs.LG2026
DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting
Yaohua Zha, Hai Wu, Shu-Tao Xia
Series decomposition has emerged as a widely acknowledged paradigm in deep time series forecasting models. However, existing approaches predominantly employ a homogeneous modeling…
cs.LG2026
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
Yaohua Zha, Chunlin Fan, Peiyuan Liu +4
Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models…