Corpus ID: 237513610

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

@article{Kamarthi2021CAMulCA,
  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={ArXiv},
  year={2021},
  volume={abs/2109.07438}
}
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 wellcalibrated 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… Expand

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