4 papers
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
Shiqiao Zhou, Zipeng Wu, Holger Schöner +3
Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent research explored retrieving relevant historical tim…
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Shiqiao Zhou, Holger Schöner, Zipeng Wu +3
Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters. Inspired…
BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
Shiqiao Zhou, Holger Schöner, Huanbo Lyu +2
Time series forecasting is a long-standing and highly challenging research topic. Recently, driven by the rise of large language models (LLMs), research has increasingly shifted fr…
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Florian Kalinke, Marco Heyden, Georg Gntuni +2
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled ap…