Univariate Time Series Prediction using Data Stream Mining Algorithms and Temporal Dependence

Marcos Alberto Mochinski, Jean Paul Barddal, Fabrício Enembreck

2022

Abstract

In this paper, we present an exploratory study conducted to evaluate the impact of temporal dependence modeling on time series forecasting with Data Stream Mining (DSM) techniques. DSM algorithms have been used successfully in many domains that exhibit continuous generation of non-stationary data. However, the use of DSM in time series is rare since they usually are univariate and exhibit strong temporal dependence. This is the main motivation for this work, such that this study mitigates such gap by presenting a univariate time series prediction method based on AdaGrad (a DSM algorithm), Auto.Arima (a statistical method) and features extracted from adjusted autocorrelation function (ACF) coefficients. The proposed method uses adjusted ACF features to convert the original series observations into multivariate data, executes the fitting process using the DSM and the statistical algorithm, and combines the AdaGrad's and Auto.Arima's forecasts to establish the final predictions. Experiments conducted with five datasets containing 141,558 time series resulted in up to 12.429% improvements in sMAPE (Symmetric Mean Average Percentage Error) error rates when compared to Auto.Arima. The results depict that combining DSM with ACF features and statistical time series methods is a suitable approach for univariate forecasting.

Paper Citation

in Harvard Style

Mochinski M., Barddal J. and Enembreck F. (2022). Univariate Time Series Prediction using Data Stream Mining Algorithms and Temporal Dependence. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 398-408. DOI: 10.5220/0010859600003116

in Bibtex Style

@conference{icaart22,
author={Marcos Alberto Mochinski and Jean Paul Barddal and Fabrício Enembreck},
title={Univariate Time Series Prediction using Data Stream Mining Algorithms and Temporal Dependence},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={398-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010859600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Univariate Time Series Prediction using Data Stream Mining Algorithms and Temporal Dependence
SN - 978-989-758-547-0
AU - Mochinski M.
AU - Barddal J.
AU - Enembreck F.
PY - 2022
SP - 398
EP - 408
DO - 10.5220/0010859600003116