Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module.

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

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.

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Main Track - Data Mining and Knowledge Management

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Fan Zhou

2

SHORT BIO

Presentations

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

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