Probabilistic Forecasting with Coherent Aggregation

• URL: http://arxiv.org/abs/2307.09797v3
• Date: Mon, 04 Nov 2024 22:25:41 GMT
• Title: Probabilistic Forecasting with Coherent Aggregation
• Authors: Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, O. Nangba Meetei, Mengfei Cao, Michael W. Mahoney,
• Abstract summary: We augment an MQForecaster neural network architecture with a novel deep Gaussian factor forecasting model that achieves coherence by construction. In a comparison to state-of-the-art coherent forecasting methods, DeepCoFactor achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%.
• Score: 42.215158938066054
• License:
• Abstract: Obtaining accurate probabilistic forecasts is an important operational challenge in many applications, like energy management, climate forecast, supply chain planning, and resource allocation. In many of these applications, there is a natural hierarchical structure over the forecasted quantities; and forecasting systems that adhere to this hierarchical structure are said to be coherent. Furthermore, operational planning benefits from accuracy at all levels of the aggregation hierarchy. Building accurate and coherent forecasting systems, however, is challenging: classic multivariate time series tools and neural network methods are still being adapted for this purpose. In this paper, we augment an MQForecaster neural network architecture with a novel deep Gaussian factor forecasting model that achieves coherence by construction, yielding a method we call the Deep Coherent Factor Model Neural Network (DeepCoFactor) model. DeepCoFactor generates samples that can be differentiated with respect to the model parameters, allowing optimization on various sample-based learning objectives that align with the forecasting system's goals, including quantile loss and the scaled Continuous Ranked Probability Score (CRPS). In a comparison to state-of-the-art coherent forecasting methods, DeepCoFactor achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%, as measured on six publicly-available forecasting datasets.

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