Shao, W.; Radke, L.F.; Sivrikaya, F.; Albayrak, S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics2021, 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.
Shao, W.; Radke, L.F.; Sivrikaya, F.; Albayrak, S. Adaptive Online Learning for the Autoregressive Integrated Moving Average Models. Mathematics2021, 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.
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
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
