research-article

Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

Authors:

Hao WangAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 4523 - 4529

https://doi.org/10.1145/3583780.3615487

Published: 21 October 2023 Publication History

Abstract

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.

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Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
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Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
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Min Zhang
Tsinghua University, China
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Rodrygo Santos
Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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October 21 - 25, 2023

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Cited By

Wang SChu ZSun YLiu YGuo YChen YJian HMa LLu XZhou JSerra ESpezzano F(2024)Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680072(4948-4956)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680072
Wang HWang ZNiu YLiu ZLi HLiao YHuang YLiu X(2024)An Accurate and Interpretable Framework for Trustworthy Process MonitoringIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33196065:5(2241-2252)Online publication date: May-2024
https://doi.org/10.1109/TAI.2023.3319606
Liu ZCao YXu HHuang YHe QChen XTang XLiu X(2024)Hidformer: Hierarchical dual-tower transformer using multi-scale mergence for long-term time series forecastingExpert Systems with Applications10.1016/j.eswa.2023.122412239(122412)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122412
Feng HXie A(2024)Attempt of Graph Neural Network Algorithm in the Field of Financial Anomaly DetectionProceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology10.1007/978-981-97-2757-5_65(616-623)Online publication date: 26-Apr-2024
https://doi.org/10.1007/978-981-97-2757-5_65
Zhou YChen ZXie A(2023)Temporal Attention Convolutional Neural Networks Based on LSTM-Encoder for Time Series Forecasting2023 International Conference on Networks, Communications and Intelligent Computing (NCIC)10.1109/NCIC61838.2023.00014(51-54)Online publication date: 17-Nov-2023
https://doi.org/10.1109/NCIC61838.2023.00014
Lu XDuan CXie ABao MWang MZhong Q(2023)Research on Graph Neural Network Algorithms for Financial Anomaly Detection2023 International Conference on Networks, Communications and Intelligent Computing (NCIC)10.1109/NCIC61838.2023.00009(18-23)Online publication date: 17-Nov-2023
https://doi.org/10.1109/NCIC61838.2023.00009

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