AI BriefingLG AI Research: Adaptive Information Routing Technology for Multimodal Time Series Forecasting
Key point
LG AI Research proposed AIR, a multimodal time series forecasting technology that reflects the characteristics of time series data and text data.
Details
At the recently held NeurIPS 2024, time series forecasting research drew significant attention. In particular, various studies such as qualitative forecasting using LLMs and multimodal forecasting were actively conducted.
LG AI Research's Data Intelligence (DI) Lab presented a new technology for multimodal time series forecasting called Adaptive Information Routing (AIR) at this NeurIPS's TSALM Workshop.
Existing multimodal forecasting approaches are largely divided into two types: directly fine-tuning a pretrained language model (PLM), or utilizing text information through a separate embedding model. However, these approaches have the limitation of not sufficiently considering the different characteristics that time series data and text data possess.
AIR technology reflects both the local, structural characteristics of time series data and the global, unstructured characteristics of text data. The key is utilizing text data as a Controller for the time series model, effectively improving forecasting accuracy while accounting for the differences between the two data types.
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