  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.
