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Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting

Authors:

Jiebo LuoAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 987 - 995

https://doi.org/10.1145/3097983.3098113

Published: 04 August 2017 Publication History

Abstract

Forecasting the future observations of time-series data can be performed by modeling the trend and fluctuations from the observed data. Many classical time-series analysis models like Autoregressive model (AR) and its variants have been developed to achieve such forecasting ability. While they are often based on the white noise assumption to model the data fluctuations, a more general Brownian motion has been adopted that results in Ornstein-Uhlenbeck (OU) process. The OU process has gained huge successes in predicting the future observations over many genres of time series, however, it is still limited in modeling simple diffusion dynamics driven by a single persistent factor that never evolves over time. However, in many real problems, a mixture of hidden factors are usually present, and when and how frequently they appear or disappear are unknown ahead of time. This imposes a challenge that inspires us to develop a Mixture Factorized OU process (MFOUP) to model evolving factors. The new model is able to capture the changing states of multiple mixed hidden factors, from which we can infer their roles in driving the movements of time series. We conduct experiments on three forecasting problems, covering sensor and market data streams. The results show its competitive performance on predicting future observations and capturing evolution patterns of hidden factors as compared with the other algorithms.

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Cited By

C B VV VPandeya M(2023)Reassessing Deep Learning in Time Series: Predicting Time with Foresight2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465902(1379-1384)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICAICCIT60255.2023.10465902
Li ZCai RNg HWinslett MFu TXu BYang XZhang Z(2021)Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical SystemsACM Transactions on Intelligent Systems and Technology10.1145/344503312:2(1-21)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1145/3445033

Index Terms

Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data streams

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Published: 04 August 2017

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mixture factorized ornstein-uhlenbeck process (mfoup)

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NSF
973 program of China

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

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Cited By

C B VV VPandeya M(2023)Reassessing Deep Learning in Time Series: Predicting Time with Foresight2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465902(1379-1384)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICAICCIT60255.2023.10465902
Li ZCai RNg HWinslett MFu TXu BYang XZhang Z(2021)Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical SystemsACM Transactions on Intelligent Systems and Technology10.1145/344503312:2(1-21)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1145/3445033

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