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.

CAMul是一种通用的概率多视图预测框架，可以从多种数据源中学习表示和不确定性，并以动态上下文特定方式将每个数据视图的知识和不确定性整合起来，用于精细地预测分布建模，并在多个领域的实验中验证了其超越其他最先进的概率预测模型超过25％的精度和校准性。

CAMul: 校准和准确的多视角时间序列预测