Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models
Al-Bossly, A. (2020). Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models. EKB Journal Management System, 2(2), 31-38. doi: 10.21608/jisse.2020.27734.1024
Afrah Al-Bossly. "Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models". EKB Journal Management System, 2, 2, 2020, 31-38. doi: 10.21608/jisse.2020.27734.1024
Al-Bossly, A. (2020). 'Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models', EKB Journal Management System, 2(2), pp. 31-38. doi: 10.21608/jisse.2020.27734.1024
Al-Bossly, A. Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models. EKB Journal Management System, 2020; 2(2): 31-38. doi: 10.21608/jisse.2020.27734.1024

Time Series Statistical Analysis by Using Artificial Neural Network techniques Case study: Forecasting Models

Journal of International Society for Science and Engineering
Article 2, Volume 2, Issue 2, June 2020, Page 31-38  XML PDF (536.56 K)
Document Type: Review Article
DOI: 10.21608/jisse.2020.27734.1024
View on SCiNiTO View on SCiNiTO
Author
Afrah Al-Bossly email
Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia.
Abstract
Artificial Intelligent is regarded as a novel tool for future statistical data estimation. As an important issue. Also, an important issue on how to evaluate weather forecasting. In this context, numerous papers are available to describe methodologies similar to neural networks (NN) regression for weather prediction (time-series application). The next study is a review about nonetheless, to perform ranking as this model is complex due to the variety of the forecasting horizon, time step, data set and performance indication. Also, the accurateness of these models is dependent on input parameters and architecture type algorithms utilized. This leads to a better understanding of the contributions to be expected from analytics. Real data from different country sites will be used while developing the model. The advance of the forecasting is proposed to support countries, future stakeholders, and engineers to select sites of weather systems to evaluate the techno-economic merits of large-scale weather ‘data integration the Objective of this work is to investigate and assess the forecasting statistical models using artificial intelligence techniques. The results indicated that ANN realized enhanced in predicting the advanced casing of the weather forecasting.
Keywords
Time Series; ANN; Forecasting; Regression
Statistics
Article View: 296
PDF Download: 103