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

Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B Aditya Prakash
2022 Proceedings of the ACM Web Conference 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 wellcalibrated 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
more » ... r each data view. We propose a general probabilistic multi-view forecasting framework CAMul, which can learn representations and uncertainty from diverse data sources. It integrates the information 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. CCS CONCEPTS • Computing methodologies → Machine learning; • Information systems → Data mining; Web mining.
doi:10.1145/3485447.3512037 fatcat:rwafoqgrmbfbbjpzysiwl6kb7q