A Study of Foundation Models for Large-scale Time-series Forecasting

Hongjie Chen, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Hoda Eldardiry

Abstract: Recent successes of foundation models in large language models have inspired researchers to apply similar technologies to time-series forecasting. Unlike conventional time-series forecasting models, which are trained on the training subset of the target dataset, foundation models are trained on a large collection of source datasets that do not necessarily include the target dataset, with the assumption that foundation models can capture the complex patterns between the input time-series values and the desired predictions. Although many foundation models have claimed superior prediction performance compared to conventional models, one question remains unanswered: Do foundation models for time-series forecasting, which train on many datasets other than the target dataset, perform better than conventional models that train on only (the training subset of) the target dataset? To answer this question, this paper adapts a diffusion-based foundation model and conducts extensive experiments using both small datasets and a large collection of over 100 datasets. Our results show that training on large-scale datasets does not necessarily guarantee a better performance than a conventional model that trains only on the dataset from the same domain. Hence, this paper provides insights for future foundation model research in large-scale time-series forecasting, emphasizing that the usage of target datasets should be considered in addition to training on large-scale source datasets.