Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Adaptive Online Learning for Time Series Prediction

Version 1 : Received: 19 April 2021 / Approved: 22 April 2021 / Online: 22 April 2021 (09:32:36 CEST)

A peer-reviewed article of this Preprint also exists.

Shao, W.; Radke, L.F.; Sivrikaya, F.; Albayrak, S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics 2021, 9, 1523. Shao, W.; Radke, L.F.; Sivrikaya, F.; Albayrak, S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics 2021, 9, 1523.

Journal reference: Mathematics 2021, 9, 1523
DOI: 10.3390/math9131523

Abstract

We study 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 inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets

Keywords

ime Series Analysis; Online Optimisation; Online Model Selection

Subject

MATHEMATICS & COMPUTER SCIENCE, General & Theoretical Computer Science

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