Extend Nearly Pseudo Quasi-2-Absorbing submodules(II)

• Published in: Ibn Al-Haitham Journal for Pure and Applied Sciences Vol. 36; no. 2; pp. 420 - 429
• Main Authors: Layla A. Ahmed, Mohammed, Monem
• Format: Journal Article
• Language: English
• Published: 20-04-2023
• ISSN: 1609-4042; 2521-3407
• DOI: 10.30526/36.2.3060

      Time series analysis is the statistical approach used to analyze a series of
data. Time series is the most popular statistical method for forecasting, which is
widely used in several statistical and economic applications. The wavelet transform is a
powerful mathematical technique that converts an analyzed signal into a time-frequency
representation. The wavelet transform method provides signal information in both the
time domain and frequency domain. The aims of this study are to propose a wavelet
function by derivation of a quotient from two different Fibonacci coefficient
polynomials, as well as a comparison between ARIMA and wavelet-ARIMA. The time series
data for daily wind speed is used for this study. From the obtained results, the
proposed wavelet-ARIMA is the most appropriate wavelet for wind speed. As compared to
wavelets the proposed wavelet is the most appropriate wavelet for wind speed
forecasting, it gives us less value of MAE and RMSE.