Forecasting with information extracted from the residuals of ARIMA in financial time series using continuous wavelet transform Online publication date: Wed, 30-Nov-2022
by Heng Yew Lee; Woan Lin Beh; Kong Hoong Lem
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 22, No. 1/2, 2023
Abstract: Time series of financial or economic data are often considered to have certain trends and patterns. It is believed that the study of historical patterns helps in the forecasting into the future. ARIMA model is one of the popular models for the task. However, long-term forecasting with ARIMA often appears as a straight line. This is due to ARIMA's dependency on previous values and its tendency to omit the outliers that lie outside of the captured general trend. This paper sought to capture useful outlier information from the residual of ARIMA modelling by using continuous wavelet transform (CWT). The CWT captured information was then added to the ARIMA forecasted values to form non-homogenous long-term forecasting. The results were encouraging. It was also found that choices of certain CWT related parameters have positive or negative effect to the forecasting outcomes.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook