Next Article in Journal
On the Verification of the Pedestrian Evacuation Model
Next Article in Special Issue
AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction
Previous Article in Journal
An Intrinsic Value Approach to Valuation with Forward–Backward Loops in Dividend Paying Stocks
Article

Adaptive Online Learning for the Autoregressive Integrated Moving Average Models

1
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
2
GT-ARC Gemeinnützige GmbH, Ernst-Reuter-Platz 7, 10587 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Freddy Gabbay, Mihai Postolache and Ioannis K. Argyros
Mathematics 2021, 9(13), 1523; https://doi.org/10.3390/math9131523
Received: 19 April 2021 / Revised: 19 May 2021 / Accepted: 24 June 2021 / Published: 29 June 2021
(This article belongs to the Special Issue Computational Optimizations for Machine Learning)
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. View Full-Text
Keywords: ARIMA model; time series analysis; online optimization; online model selection ARIMA model; time series analysis; online optimization; online model selection
Show Figures

Figure 1

MDPI and ACS Style

Shao, W.; Radke, L.F.; Sivrikaya, F.; Albayrak, S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics 2021, 9, 1523. https://doi.org/10.3390/math9131523

AMA Style

Shao W, Radke LF, Sivrikaya F, Albayrak S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics. 2021; 9(13):1523. https://doi.org/10.3390/math9131523

Chicago/Turabian Style

Shao, Weijia, Lukas F. Radke, Fikret Sivrikaya, and Sahin Albayrak. 2021. "Adaptive Online Learning for the Autoregressive Integrated Moving Average Models" Mathematics 9, no. 13: 1523. https://doi.org/10.3390/math9131523

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop