Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction

Abstract

We consider a distributed time series forecasting problem where multiple distributed nodes each observing a local time series (of potentially different modality) collaborate to make both local and global forecasts. This problem is particularly challenging because each node only observes time series generated from a subset of sources, making it challenging to utilize correlations among different streams for accurate forecasting; and the data streams observed at each node may represent different modalities, leading to heterogeneous computational requirements among nodes. To tackle these challenges, we propose a hierarchical learning framework, consisting of multiple local models and a global model, and provide a suite of efficient training algorithms to achieve high local and global forecasting accuracy. We theoretically establish the convergence of the proposed framework and demonstrate the effectiveness of the proposed approach using several time series forecasting tasks, with the (somewhat surprising) observation that the proposed distributed models can match, or even outperform centralized ones.