6 papers
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
Yanru Sun, Zongxia Xie, Emadeldeen Eldele +3
Time series forecasting, which aims to predict future values based on historical data, has garnered significant attention due to its broad range of applications. However, real-worl…
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…
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…
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…
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…
Hierarchical Classification Auxiliary Network for Time Series Forecasting
Yanru Sun, Zongxia Xie, Dongyue Chen +2
Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Squar…