Data preparation is a crucial step in data analysis, serving as the foundation for successful data mining. To
uncover novel insights from existing databases, it is essential to ensure data completeness, quality, and real-world
relevance. However, missing values can hinder analysis and application to new data, necessitating the employment of
statistical techniques during data preparation. By leveraging statistical methods, we can address data incompleteness
and ambiguity. This paper presents two sequential approaches for imputing missing attribute values, focusing on
numerical variables in time series data using the moving average method. A comparative study of both methods is
provided, highlighting their effectiveness in recovering missing data.
[1] Prof. Nidhi S Bhavsar, Dr. Khushbu, Dr. Darshanaben Dipakkumar Pandya , " A New Clustering Approach for
Anomaly Intrusion Detection, International Journal of Scientific Research in Science and Technology(IJSRST),
Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 11, Issue 1, pp.127-134.
[2] Nidhi S Bhavsar, Dr. Khushbu “Closest fit Approach for A typical Value Revealing and Deciles Range Anomaly
Detection Method for Recovering Misplaced value in Data Mining”, International Journal of Scientific Research in
Computer Science, Engineering and Information Technology (IJSRCSEIT), Volume 8, Issue 5 September-October
2022 ,p.p-217-222
[3] S. Gaur, M.S. Dulawat, “A perception of statistical inference in data mining” Int. J. Comput. Sci. Commun. 1(2),
653–658 (2010)
[4] J.W. Grzymala-Busse, “Data with missing attribute values: generalization of in-discernibility”
[5] D.B. Rubin, Inference and missing data. Biometrika 63, 581–592 (1976)
[6] Realtion and rules induction, Transactions of rough sets. Lect. Notesin Comput. Sci. J. Subline, 1, 8–95 (2004).
(Springer-Verlag)
[7] Chen, L., Drane, M.T., Valois, R.F., and Drane, J.W., “Multiple imputation for missing ordinal data”, Journal of
Modern Applied Statistical Methods, Vol. 4, No.1, pp. 288-299, 2005.
[8] Rubin, D.B., Inference and missing data, Biometrika, 63, pp. 581-592 (1976).
[9] P.D. Allison, Estimation of Linear Models with Incomplete data, Social Methodology (Jossey Bass, San Francisco,
1987), pp. 71–103
[10] L. Chen, M.T. Drane, R.F. Valois, J.W. Drane, Multiple imputation for missing ordinal data. J. Mod. Appl. Stat.
Methods 4(1), 288–299 (2005)
