Authors:
Marcos Alberto Mochinski
;
Jean Paul Barddal
and
Fabrício Enembreck
Affiliation:
Graduate Program in Informatics, PPGIa, Escola Politécnica, Pontifícia Universidade Católica do Paraná, PUCPR Curitiba, Brazil
Keyword(s):
Time Series Forecasting, Data Stream Mining Algorithms, Multiple Time Series, Ensemble, Feature Engineering, Temporal Dependence.
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. Experim
ents 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.
(More)