4 papers
Causal Semantic Alignment for LLM-based Time Series Forecasting
Kexuan Zhang, Xiaobei Zou, Cesare Alippi +2
Recent advances in Large Language Models (LLMs) have opened new possibilities for time series forecasting by enabling alignment between temporal patterns and pretrained word embedd…
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Xiaobei Zou, Luolin Xiong, Kexuan Zhang +2
Spatio-temporal forecasting remains challenging under non-stationary environments because both data distributions and spatial relations evolve over time. Temporal normalization and…
Caformer: Rethinking Time Series Analysis from Causal Perspective
Kexuan Zhang, Xiaobei Zou, Yang Tang
Time series analysis is a vital task with broad applications in various domains. However, effectively capturing cross-dimension and cross-time dependencies in non-stationary time s…
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Xiaobei Zou, Luolin Xiong, Yang Tang +1
Spatio-temporal forecasting in various domains, like traffic prediction and weather forecasting, is a challenging endeavor, primarily due to the difficulties in modeling propagatio…