5 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Efficient Differentiable Approximation of Generalized Low-rank Regularization
Naiqi Li, Yuqiu Xie, Peiyuan Liu +3
Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under…
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Peiyuan Liu, Hang Guo, Tao Dai +5
Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a…
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Naiqi Li +5
Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourie…