Using Pre-trained LLMs for Multivariate Time Series Forecasting

Time series forecasting refers to a class of techniques for the prediction of events through a sequence of time, typically to inform strategic or tactical decision making. Going beyond strategic forecasting problems (e.g., those commonly-used historically in statistics and econometrics [1]), operational forecasting problems are increasingly-important. For example, at large internet retail companies, this includes demand forecasting for products at an online retailer, work force cohorts of a company in its locations, compute capacity needs per region and server type, etc.; in scientific machine learning, this includes prediction of extreme events in, e.g., climate and weather models; and so on. In particular, MQCNN [2] and MQTransformer [3] are stateof-the-art (SOTA) neural network (NN) based multivariate time series forecasting models that are used to predict future demand at the product level for hundreds of millions of products.