5 papers
Rethinking Multimodal Time-Series Forecasting Evaluation
Haoxin Liu, Yichen Zhou, Rajat Sen +2
We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world time series with diverse doma…
Evolutionary Feature Engineering for Structured Data
Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz +4
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using…
Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
Haoxin Liu, Yichen Zhou, Rajat Sen +2
Time-Series Foundation Models (TSFMs) excel at zero-shot unimodal forecasting using numerical data, but unlike LLMs they cannot consume multimodal, non-numerical context that often…
Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
Kai Kim, Howard Tsai, Rajat Sen +5
Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset.…
In-Context Fine-Tuning for Time-Series Foundation Models
Abhimanyu Das, Matthew Faw, Rajat Sen +1
Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for of a time-series foundati…