In:
Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 37, No. 9 ( 2023-06-26), p. 11417-11425
Abstract:
Multivariate time series forecasting with hierarchical structure
is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities,
states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation.
In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency,
but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration
mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure
to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely
on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only
achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based
targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream
tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior
performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization
networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraints.
Type of Medium:
Online Resource
ISSN:
2374-3468
,
2159-5399
DOI:
10.1609/aaai.v37i9.26350
Language:
Unknown
Publisher:
Association for the Advancement of Artificial Intelligence (AAAI)
Publication Date:
2023
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