Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Since, the industrial data plays significant element in any economic growth and these data have many factors that effect on its behavior. Therefore, in this study events of productivity of the extractive industry in Jordan will be explored and forecasted using some of traditional model which is (ARIMA Model and exponential model) compound with Orthogonal Wavelet Transform (OWT) in order to improve the forecasting accuracy. First, the series of dataset will be decomposed by OWT’s functions in order to capture the significant affect based on detailed coefficients, then the smooth’s series will be predicted using ARIMA Model, exponential model, OW+ARIMA Model and Exponential+OWT Model in order to improve the forecasting accuracy.
How to cite this article:
S. Al-Wadi and Abed H. Al-Slaihat. Industrial Data Decomposition and Forecasting Using Discrete Wavelet Transform.
DOI: https://doi.org/10.36478/jeasci.2019.4303.4306
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.4303.4306