CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

TL;DR

CAMul is a probabilistic multi-view forecasting framework that effectively models uncertainty from diverse data sources, improving accuracy and calibration across multiple domains.

Contribution

It introduces a novel method for integrating and weighting multiple data views with uncertainty modeling for better probabilistic forecasts.

Findings

Outperforms state-of-the-art models by over 25% in accuracy.

Achieves well-calibrated forecast distributions.

Effective across various domains and data modalities.

Abstract

Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information as well as uncertainty from these data sources for well-calibrated and accurate forecasts is an important challenging problem. Most previous work on multi-modal learning and forecasting simply aggregate intermediate representations from each data view by simple methods of summation or concatenation and do not explicitly model uncertainty for each data-view. We propose a general probabilistic multi-view forecasting framework CAMul, that can learn representations and uncertainty from diverse data sources. It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution. We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.