Annual Forecasting Using a Hybrid Approach

• Main Author: Qais Mustafa Abdulqader
• Format: Article
• Language: English
• Published: Refaad 2018-04-01
• Series: General Letters in Mathematics
• Subjects: ARIMA; De-noising; Forecasting; Time series; Wavelet transforms 2000 MSC No: 97K80; 65T60; 37M10.
• Online Access: http://www.refaad.com/Files/glm/GLM-4-2-5.pdf

In this paper, we used a hybrid method based on wavelet transforms and ARIMA models and applied on the time series annual data of rain precipitation in the Province of Erbil-Iraq in millimeters. A sample size has been taken during the period 1970 - 2014.We intended to obtain the ability to explain how the hybrid method can be useful when making a forecast of time series and how the quality of forecasting can be enhanced through applying it on actual data and comparing the classical ARIMA method and our suggested method depending on some statistical criteria. Results of the study proved an advantage of the statistical hybrid method and showed that the forecast error could be reduced when applying Wavelet-ARIMA technique and this helps to give the enhancement of forecasting of the classical model. In addition, it was found that out of wavelet families, Daubechies wavelet of order two using fixed form thresholding with soft function is very suitable when de-noising the data and performed better than the others. The annual rainfall in Erbil in the coming years will be close to 370 millimeters