44 citations · 46 across the 8 of their papers we have counts for
8 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…
Transformers can optimally learn regression mixture models
Reese Pathak, Rajat Sen, Weihao Kong +1
Mixture models arise in many regression problems, but most methods have seen limited adoption partly due to these algorithms' highly-tailored and model-specific nature. On the othe…
A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen +1
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot…
Linear Regression using Heterogeneous Data Batches
Ayush Jain, Rajat Sen, Weihao Kong +2
In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relations…