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Impact of Starting Outlier Removal on Accuracy of Time Series Forecasting


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[1] Astola J, Kuosmanen P., Fundamentals of Nonlinear Digital Filtering, CRC Press, 1997. Search in Google Scholar

[2] Box G., Jenkins G., Time Series Analysis: Forecasting and Control, Holden-day, San Francisco, 1970. Search in Google Scholar

[3] Cleveland W. S., Devlin S. J., Locally-weighted regression: an approach to regression analysis by local fitting, ‘Journal of the American Statistical Association’, 1988, Vol. 83, Iss. 403, pp. 596 — 610.10.1080/01621459.1988.10478639 Search in Google Scholar

[4] Cleveland W. S., Robust locally weighted regression and smoothing scatterplots, ‘Journal of the American Statistical Association’, 1979, Vol. 74, Iss. 368, pp. 829 — 836.10.1080/01621459.1979.10481038 Search in Google Scholar

[5] Davies L, Gather U., The identification of multiple outliers, ‘Journal of the American Statistical Association’, 1993, Vol. 88, Iss. 423, 782 — 792.10.1080/01621459.1993.10476339 Search in Google Scholar

[6] Edwards R. E., Functional Analysis. Theory and Applications, Hold, Rinehart and Winston, 1965. Search in Google Scholar

[7] Fox J., Weisberg S., An R Companion to Applied Regression (3rd ed.), SAGE, 2018. Search in Google Scholar

[8] Gubner J., Probability and Random Processes for Electrical and Computer Engineers, Cam-bridge University Press, 2006.10.1017/CBO9780511813610 Search in Google Scholar

[9] Hamilton J. D., Time Series Analysis, Princeton University Press, Princeton, NJ, 1994. Search in Google Scholar

[10] Han J., Kamber M., Pei J., 12. Outlier detection, in: Data Mining: Concepts and Techniques (Third Edition), Morgan Kaufmann, 2012, pp. 543 — 584.10.1016/B978-0-12-381479-1.00012-5 Search in Google Scholar

[11] Hyndman R., Koehler A., Another look at measures of forecast accuracy, ‘International Jour-nal of Forecasting’, 2006, Vol. 22, Iss. 4, pp. 679 — 688.10.1016/j.ijforecast.2006.03.001 Search in Google Scholar

[12] Kotu V., DeshpandeB., Data Science (Second Edition), MorganKaufmann, 2019. Search in Google Scholar

[13] Mills T. C., Chapter 8. Unobserved Component Models, Signal Extraction, and Filters, in:Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, Academic Press, 2019, pp. 131—144.10.1016/B978-0-12-813117-6.00008-9 Search in Google Scholar

[14] Pankratz A., Forecasting with Univariate Box—Jenkins Models:Concepts and Cases, JohnWiley& Sons, 1983.10.1002/9780470316566 Search in Google Scholar

[15] Papoulis A., Probability, Random variables and Stochastic processes, McGraw-Hill, 1991. Search in Google Scholar

[16] Randel W. J., Filtering and Data Preprocessing for Time Series Analysis, ‘Methods in Experimental Physics’, 1994, Vol. 28, pp. 283—311.10.1016/S0076-695X(08)60260-4 Search in Google Scholar

[17] Romanuke V.V., Theoretic-game methods of identification of models for multistage technical control and run-in under multivariate uncertainties, Mathematical Modeling andComputational Methods, Vinnytsia National Technical University, Vinnytsia, Ukraine, 2014. Search in Google Scholar

[18] Romanuke V.V., Identification of the machining tool wear model via minimax combining and weighting subsequently specific models,‘Information processing systems’, 2015, Iss.12 (137), pp. 106—111. Search in Google Scholar

[19] Romanuke V. V., Meta-minimax approach for optimal alternatives subset regarding the change of the risk matrix in ensuring industrial and manufacturing labor safety,‘Herald ofKhmelnytskyi national university. Technicalsciences’, 2015, No.6,pp. 97—99. Search in Google Scholar

[20] RomanukeV. V., Appropriateness of DropOut layers and allocation of their 0.5 rates across convolutional neural networks for CIFAR-10, EEACL26, and NORB datasets,‘Applied Computer Systems’, 2017, Vol. 22, pp. 54—63.10.1515/acss-2017-0018 Search in Google Scholar

[21] Romanuke V. V., An attempt of finding an appropriate number of convolutional layers in CNNs based on benchmarks of heterogeneous datasets,‘Electrical, Control and Communication Engineering’, 2018, Vol. 14, No. 1,pp. 51—57.10.2478/ecce-2018-0006 Search in Google Scholar

[22] Romanuke V. V., Decision making criteria hybridization for finding optimal decisions’subset regarding changes of the decision function,‘Journal of Uncertain Systems’, 2018, Vol. 12, No.4, pp. 279—291. Search in Google Scholar

[23] Romanuke V. V., Minimal total weighted tardiness in tight-tardy single machine preemptive idling-free scheduling,‘Applied ComputerSystems’,2019, Vol. 24, No.2, pp. 150—160.10.2478/acss-2019-0019 Search in Google Scholar

[24] Romanuke V.V., A minimax approach to mapping partial interval uncertainties into point estimates, ‘Journal of Mathematics and Applications’, 2019, Vol. 42, pp. 147—185.10.7862/rf.2019.10 Search in Google Scholar

[25] Romanuke V. V., Wind speed distribution directapproximationbyaccumulative statisticsof measurements and root-mean-square deviation control,‘Electrical, Control and Communica-tion Engineering’, 2020, Vol. 16, No. 2, pp. 65—71.10.2478/ecce-2020-0010 Search in Google Scholar

[26] Savitzky A., Golay M. J. E., Smoothing and differentiation of data by simplified least squares procedures, ‘Analytical Chemistry’, 1964, Vol. 36, Iss. 8, pp. 1627—1639.10.1021/ac60214a047 Search in Google Scholar

[27] Schelter B., Winterhalder M., Timmer J., Handbook of Time Series Analysis:Recent Theoretical Developments and Applications, Wiley,2006.10.1002/9783527609970 Search in Google Scholar

[28] Zhao Y., Chapter 7. Outlier detection, in:Rand DataMining:Examples and Case Studies, Academic Press, 2013, pp. 63—73.10.1016/B978-0-12-396963-7.00007-6 Search in Google Scholar