Probabilistic Forecasting with Coherent Aggregation
Obtaining accurate probabilistic forecasts is an important operational challenge in many applications, perhaps most obviously in energy management, climate forecasting, supply chain planning, and resource allocation. In many of these applications, there is a natural hierarchical structure over the f...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
19.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Obtaining accurate probabilistic forecasts is an important operational
challenge in many applications, perhaps most obviously in energy management,
climate forecasting, 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 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 gains between 4.16 and 54.40%, as measured on
three publicly available hierarchical forecasting datasets. |
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DOI: | 10.48550/arxiv.2307.09797 |