Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data

• URL: http://arxiv.org/abs/2411.06735v2
• Date: Thu, 21 Nov 2024 00:52:53 GMT
• Title: Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
• Authors: Kai Kim, Howard Tsai, Rajat Sen, Abhimanyu Das, Zihao Zhou, Abhishek Tanpure, Mathew Luo, Rose Yu,
• Abstract summary: Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series. We develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare.
• Score: 23.10730301634422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare. Our data is a significant contribution to the rare selection of available multimodal datasets. We also propose the Hybrid Multi-Modal Forecaster (Hybrid-MMF), a multimodal LLM that jointly forecasts both text and time series data using shared embeddings. However, contrary to our expectations, our Hybrid-MMF model does not outperform existing baselines in our experiments. This negative result highlights the challenges inherent in multimodal forecasting. Our code and data are available at https://github.com/Rose-STL-Lab/Multimodal_ Forecasting.

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