Online learning of ARIMA for time series prediction

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, wh...

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Main Authors: LIU, Chenghao, HOI, Steven C. H., ZHAO, Peilin, SUN, Jianling
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3411
https://ink.library.smu.edu.sg/context/sis_research/article/4412/viewcontent/OnlinelearningofARIMAfortimeseriesprediction.pdf
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spelling sg-smu-ink.sis_research-44122017-01-26T07:43:12Z Online learning of ARIMA for time series prediction LIU, Chenghao HOI, Steven C. H., ZHAO, Peilin SUN, Jianling Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to reformulate the ARIMA model into a task of full information online optimization (without random noise terms). As a consequence, we can online estimation of the parameters in an efficient and scalable way. Furthermore, we analyze regret bounds of the proposed algorithms, which guarantee that our online ARIMA model is provably as good as the best ARIMA model in hindsight. Finally, our encouraging experimental results further validate the effectiveness and robustness of our method. 2016-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3411 https://ink.library.smu.edu.sg/context/sis_research/article/4412/viewcontent/OnlinelearningofARIMAfortimeseriesprediction.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
Theory and Algorithms
spellingShingle Computer Sciences
Databases and Information Systems
Theory and Algorithms
LIU, Chenghao
HOI, Steven C. H.,
ZHAO, Peilin
SUN, Jianling
Online learning of ARIMA for time series prediction
description Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to reformulate the ARIMA model into a task of full information online optimization (without random noise terms). As a consequence, we can online estimation of the parameters in an efficient and scalable way. Furthermore, we analyze regret bounds of the proposed algorithms, which guarantee that our online ARIMA model is provably as good as the best ARIMA model in hindsight. Finally, our encouraging experimental results further validate the effectiveness and robustness of our method.
format text
author LIU, Chenghao
HOI, Steven C. H.,
ZHAO, Peilin
SUN, Jianling
author_facet LIU, Chenghao
HOI, Steven C. H.,
ZHAO, Peilin
SUN, Jianling
author_sort LIU, Chenghao
title Online learning of ARIMA for time series prediction
title_short Online learning of ARIMA for time series prediction
title_full Online learning of ARIMA for time series prediction
title_fullStr Online learning of ARIMA for time series prediction
title_full_unstemmed Online learning of ARIMA for time series prediction
title_sort online learning of arima for time series prediction
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3411
https://ink.library.smu.edu.sg/context/sis_research/article/4412/viewcontent/OnlinelearningofARIMAfortimeseriesprediction.pdf
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