S.  Al-Wadi, Abed H.  Al-Slaihat, 
    Industrial Data Decomposition and Forecasting Using Discrete Wavelet Transform,
    Journal of Engineering and Applied Sciences,
    Volume 14,Issue 13,
    2019,
    Pages 4303-4306,
    ISSN 1816-949x,
    jeasci.2019.4303.4306,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2019.4303.4306)
    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.
    Keywords: Orthogonal wavelet transform;ARIMA Model;exponential model;industry data;forecasting significant affect;behaviour