Adaptive Online Learning for the Autoregressive Integrated Moving Average Models

Weijia Shao, Lukas Friedemann Radke, Fikret Sivrikaya and Sahin Albayrak
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Weijia Shao: Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
Lukas Friedemann Radke: Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
Fikret Sivrikaya: GT-ARC Gemeinnützige GmbH, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
Sahin Albayrak: Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany

Mathematics, 2021, vol. 9, issue 13, 1-30

Abstract: This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models without hyperparameters. The regret analysis and experiments on both synthetic and real-world datasets show that the performance of the proposed algorithms can be guaranteed in both theory and practice.

Keywords: ARIMA model; time series analysis; online optimization; online model selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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