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Comparative Study on Performance Analysis of Time Series Predictive Models
Basha S.M, Zhenning Y, , Caytiles R.D, Iyengar N.C.S.N.
Published in NADIA
Volume: 10
Issue: 8
Pages: 37 - 48
Time series models the analyses of data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time. Here five-time series datasets with different range of observation are considered to study its performance. In this paper, moving averages (MA) of series with different periods to average over are calculated; plotted series for forecasted data against original data; compared the performance of HOLT-WINTERS with the Auto Regressive Integrated Moving Average (ARIMA) model with non-zero mean; and computed the statistic test to examining the null hypothesis for the considered time series datasets. © 2017 SERSC Australia.
About the journal
JournalInternational Journal of Grid and Distributed Computing
Open Access0