6 papers · 1 filter
SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
Feng Xiong, Zongxia Xie, Yanru Sun +2
Effective multivariate time series forecasting often benefits from accurately modeling complex inter-variable dependencies. However, existing attention- or graph-based methods face…
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
Yanru Sun, Emadeldeen Eldele, Zongxia Xie +5
Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks…
Lightweight Channel-wise Dynamic Fusion Model: Non-stationary Time Series Forecasting via Entropy Analysis
Tianyu Jia, Zongxia Xie, Yanru Sun +2
Non-stationarity is an intrinsic property of real-world time series and plays a crucial role in time series forecasting. Previous studies primarily adopt instance normalization to…
LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
Wenzhe Niu, Zongxia Xie, Yanru Sun +3
Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main chal…
Patch-wise Structural Loss for Time Series Forecasting
Dilfira Kudrat, Zongxia Xie, Yanru Sun +2
Time-series forecasting has gained significant attention in machine learning due to its crucial role in various domains. However, most existing forecasting models rely heavily on p…
PPGF: Probability Pattern-Guided Time Series Forecasting
Yanru Sun, Zongxia Xie, Haoyu Xing +2
Time series forecasting (TSF) is an essential branch of machine learning with various applications. Most methods for TSF focus on constructing different networks to extract better…