TY  - JOUR
T1  - Industrial Data Decomposition and Forecasting Using Discrete Wavelet Transform
AU - Al-Wadi, S. AU - Al-Slaihat, Abed H. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 13
SP  - 4303
EP  - 4306
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.4303.4306
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.4303.4306
KW  - Orthogonal wavelet transform
KW  -ARIMA Model
KW  -exponential model
KW  -industry data
KW  -forecasting significant affect
KW  -behaviour
AB  - 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.
ER  -