1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
A SSM is Polymerized from Multivariate Time Series
Haixiang Wu
For multivariate time series (MTS) tasks, previous state space models (SSMs) followed the modeling paradigm of Transformer-based methods. However, none of them explicitly model the…
Revisiting Attention for Multivariate Time Series Forecasting
Haixiang Wu
Current Transformer methods for Multivariate Time-Series Forecasting (MTSF) are all based on the conventional attention mechanism. They involve sequence embedding and performing a…