- 分享主题:CAMul
- 论文标题:CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
- 论文链接:https://ask.qcloudimg.com/draft/8440711/htug9xrpin.pdf
- 分享人:唐共勇

1. Summary

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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 does not explicitly model uncertainty for each data view. This paper proposes 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.

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