Running Mean Smoothing Technique of Extreme Exploratory Data Analysis with Application Using R Software
DOI:
https://doi.org/10.62933/r0e91705Keywords:
Exploratory data analysis, Running means, Smoothing, Extreme dataAbstract
Exploratory Data Analysis (EDA) is a pivotal stage in statistical modeling and data analysis, comprising methods that support the identification of unknown patterns in data observations. Among the techniques employed, running mean smoothing, that sometimes known as the moving averages smoother, is openly applied to minimize the passing variations and highlight long-term trends. This paper clarifies the application of the running means smoothing method using the R software. The present way requires replacing each data observation with the average of its neighborhood within a known span, thereby detecting errors and outliers and enhancing the ability to interpret. The analysis shows how R opens the door to elastic and efficient achievement of smoothing for both contrived and real data. The outcomes proved that running mean as a technique of smoothing is effective in lighting invisible structures, detecting outliers, and providing insights into temporal dynamics, making it an important instrument in EDA.
References
[1] Husain, Q. N., Adam, M. B., Shitan, M., & Fitrianto, A. (2017). Exploratory Extreme Data Analysis for Farmer Mac Data. Malaysian Journal of Mathematical Sciences, 11, 1-16.
[2] Husain, Q. N., Adam, M. B., Shitan, M., & Fitrianto, A. (2016). Extension of Tukey’s smoothing techniques. Indian J Sci Technol, 9(28), 1-5.
[3] Morgenthaler, S. (2009). Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 33-44.
[4] Velleman, P. F. (1982). Applied nonlinear smoothing. Sociological Methodology, 13, 141-177.
[5] Hyndman, R. J. (2025). Moving averages. In International encyclopedia of statistical science (pp. 1559-1562). Berlin, Heidelberg: Springer Berlin Heidelberg.
[6] Somorjai, R. L., & Jarmasz, M. (1999). Exploratory data analysis of fMR images: Philosophy, strategies, tools, implementation. NeuroImage, 9, S45-S45.
[7] Härdle, W. (2012). Smoothing techniques: with implementation in S. Springer Science & Business Media.
[8] Deveaux, R. D. (1999). Applied smoothing techniques for data analysis.
[9] Noori, N. A., & Mohammad, A. A. (2021). Dynamical approach in studying GJR-GARCH (Q, P) models with application. Tikrit Journal of Pure Science, 26(2), 145-156.
[10] Abdullah, H. H., Khalaf, N. S., & Noori, N. A. (2024). Comparison of non-linear time series models (Beta-t-EGARCH and NARMAX models) with Radial Basis Function Neural Network usingReal Data. Iraqi Journal For Computer Science and Mathematics, 5(3), 38.
[11] Abdullah, H. H., Noori, N. A., Mohammad, A. A., Khaleel, M. A., & Khalaf, N. S. (2025). Improving Financial Volatility Modeling Using neutrosophic Logic and Applying the GJR-GARCH Model. Passer Journal of Basic and Applied Sciences, 7(2), 678-687.
[12] Ibrahim, M. Q., Mohammed, A. A., Khalaf, A. A., & Noori, N. A. (2025). Comparative Analysis of Seasonal Mixed Integrated Autoregressive Moving Average and Feed-Forward Neural Networks Models in Predicting United State Natural Hazard Casualties. Baghdad Science Journal, 22(7), 2360-2374.
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Copyright (c) 2026 Qasim N. Husain (Author)
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