1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
cs.LG2024
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…
cs.LG2024★ 1 cited
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…