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

@article{Kamarthi2022CAMulCA,
  title={CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting},
  author={Harshavardhan Kamarthi and Lingkai Kong and Alexander Rodr'iguez and Chao Zhang and B. Aditya Prakash},
  journal={Proceedings of the ACM Web Conference 2022},
  year={2022}
}
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 from these data sources for accurate and well-calibrated forecasts is an important but challenging problem. Most previous works on multi-view time-series forecasting aggregate features from each data view by simple summation or concatenation and do not explicitly model uncertainty… 

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