CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
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DOI：
10.1145/3485447.3512037
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发表时间：
2021-09
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通讯作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
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作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
概率时间序列预测可以在许多领域做出可靠的决策。大多数预测问题都有不同的数据源，包含多种模式和结构。利用这些数据源中的信息进行准确且经过良好校准的预测是一个重要但具有挑战性的问题。大多数先前的多视图时间序列预测工作通过简单的求和或串联来聚合每个数据视图的特征，并且没有明确地对每个数据视图的不确定性进行建模。我们提出了一种通用的概率多视图预测框架 CAMul，它可以从不同的数据源中学习表示和不确定性。它以动态的特定于上下文的方式集成来自每个数据视图的信息和不确定性，更加重视有用的视图来建模经过良好校准的预测分布。我们将 CAMul 用于具有不同来源和模式的多个领域，并表明 CAMul 在准确性和校准方面优于其他最先进的概率预测模型超过 25%。
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 for 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.