A Proposed Wavelet and Forecasting Wind Speed with Application

• Published in: Ibn Al-Haitham Journal for Pure and Applied Sciences Vol. 36; no. 2
• Main Authors: Layla A. Ahmed, Monem Mohammed
• Format: Journal Article
• Language: English
• Published: University of Baghdad 01-04-2023
• Subjects: ARIMA, Fibonacci Coefficient Polynomials, Proposed Wavelet, Time Series, Wavelet Transform
• 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.