Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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TIME-SERIES FORECASTING FOR SOME STATISTICAL MODELS

Authors

  • Alya Al Mutairi

Keywords:

time-series analysis, forecasting, mathematical model, prediction, minimum mean square error, prediction intervals.

DOI:

https://doi.org/10.17654/0972361722051

Abstract

Time-series forecasting (prediction) is one of the essential aims of ‘time-series analysis’, which leads to the use of the mathematical model to predict future values based on the history of the time-series. In this paper, time-series analysis is discussed to derive meaningful statistics from the data. This study examines the prediction point and intervals of time-series for some models such as AR, MA and ARMA(p, q). The minimum mean square error forecast is also presented.

Received: February 15, 2022
Revised: June 11, 2022
Accepted: June 18, 2022

References

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Sameer Sharawi, Introduction of Time Series Analysis, Arabic ed., Publication Center in King Abdulaziz University, Jeddah, Saudi Arabia, 2005.

J. D. Hamilton, State-space models, Handbook of Econometrics 4 (1994), 3039-3080.

D. Harvey, The spatial fix-Hegel, von Thunen, and Marx, Antipode 13(3) (1981), 1-12.

M. H. Alsharif, M. K. Younes and J. Kim, Time series ARIMA model for prediction of daily and monthly average global solar radiation: the case study of Seoul, South Korea, Symmetry 11(2) (2019), 240.

M. Rhif, A. Ben Abbes, I. R. Farah, B. Martínez and Y. Sang, Wavelet transform application for/in non-stationary time-series analysis: a review, Appl. Sci. 9(7) (2019), 1345.

Published

24-09-2025

Issue

Section

Articles

How to Cite

TIME-SERIES FORECASTING FOR SOME STATISTICAL MODELS. (2025). Advances and Applications in Statistics , 78, 83-92. https://doi.org/10.17654/0972361722051

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