Impact of starting outlier removal on accuracy of time series forecast ING

• Autorzy: Romanuke Vadim

Identyfikatory

DOI

10.2478/sjpna-2022-0001

Warianty tytułu

PL

Wpływ usunięcia początkowej wartości odstającej na dokładność prognozowania szeregów czasowych

• Języki publikacji: EN

Abstrakty

EN

The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is effectively removed only by making it equal to the second time point value. The forecasting accuracy is significantly improved after the removal. The favorable impact of the starting outlier removal on the time series forecasting accuracy is strong. It is the least favorable for time series with exponential rising. In the worst case of a time series, on average only 7 % to 11 % forecasts after the starting outlier removal are worse than they would be without the removal.

PL

Wartość odstająca w punkcie początkowym jednowymiarowego szeregu czasowego negatywnie wpływa na dokładność prognozowania. W ramach przeprowadzonych badań dokonano analizy wpływu usunięcia wartości odstającej poprzez zrównanie jej z wartością drugiego punktu cza-sowego. Uzyskane wyniki wskazują, że przyjęta metoda znacznie poprawia dokładność progno-zowania dla większości szeregów czasowych. Jednak w przypadku szeregów czasowych z wykładniczym wzrostem, metoda okazała się mniej skuteczna. Minimalny wzrost dokładności prognozowania wynosił w tym przypadku od 7 % do 11 %.

Słowa kluczowe

EN

time series forecasting; outlier; ARIMA; forecasting accuracy; RMSE

PL

prognozowanie szeregów czasowych; wartość odstająca; ARIMA,; dokładność prognozowania; RMSE

• Wydawca: Akademia Marynarki Wojennej im. Bohaterów Westerplatte
• Czasopismo: Maritime Technical Journal
• Rocznik: 2022
• Tom: nr 1 (224)
• Strony: 1--15
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Romanuke Vadim

• Polish Naval Academy, Faculty of Mechanical and Electrical Engineering, Śmidowicza 69 Str., 81-127 Gdynia, Poland

• https://orcid.org/0000-0003-3543-3087+

Bibliografia

• {1] Astola J, Kuosmanen P., Fundamentals of Nonlinear Digital Filtering, CRC Press, 1997.
• [2] Box G., Jenkins G., Time Series Analysis: Forecasting and Control, Holden-day, San Francisco, 1970.
• [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.
• [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.
• [5] Davies L, Gather U., The identification of multiple outliers, ‘Journal of the American Statistical Association’, 1993, Vol. 88, Iss. 423, 782 — 792.
• [6] Edwards R. E., Functional Analysis. Theory and Applications, Hold, Rinehart and Winston, 1965.
• [7] Fox J., Weisberg S., An R Companion to Applied Regression (3rd ed.), SAGE, 2018.
• [8] Gubner J., Probability and Random Processes for Electrical and Computer Engineers, Cam-bridge University Press, 2006.
• [9] Hamilton J. D., Time Series Analysis, Princeton University Press, Princeton, NJ, 1994.
• [10] Han J., Kamber M., Pei J., 12. Outlier detection, in: Data Mining: Concepts and Techniques (Third Edition), Morgan Kaufmann, 2012, pp. 543 — 584.
• [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.
• [12] Kotu V., Deshpande B., Data Science (Second Edition), Morgan Kaufmann, 2019.
• [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.
• {14] Pankratz A., Forecasting with Univariate Box — Jenkins Models: Concepts and Cases, John Wiley & Sons, 1983.
• [15] Papoulis A., Probability, Random variables and Stochastic processes, McGraw-Hill, 1991.
• [16] Randel W. J., Filtering and Data Preprocessing for Time Series Analysis, ‘Methods in Experi-mental Physics’, 1994, Vol. 28, pp. 283 — 311.
• [17] Romanuke V. V., Theoretic-game methods of identification of models for multistage technical control and run-in under multivariate uncertainties, Mathematical Modeling and Computa-tional Methods, Vinnytsia National Technical University, Vinnytsia, Ukraine, 2014.
• [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.
• [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 of Khmelnytskyi national university. Technical sciences’, 2015, No. 6, pp. 97 — 99.
• [20] Romanuke V. V., Appropriateness of DropOut layers and allocation of their 0.5 rates across convolutional neural networks for CIFAR-10, EEACL26, and NORB datasets, ‘Applied Comput-er Systems’, 2017, Vol. 22, pp. 54 — 63.
• [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 En-gineering’, 2018, Vol. 14, No. 1,pp. 51 — 57.
• [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.
• [23] Romanuke V. V., Minimal total weighted tardiness in tight-tardy single machine preemptive idling-free scheduling, ‘Applied Computer Systems’, 2019, Vol. 24, No. 2, pp. 150 — 160.
• [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.
• [25] Romanuke V. V., Wind speed distribution direct approximation by accumulative statistics of measurements and root-mean-square deviation control, ‘Electrical, Control and Communica-tion Engineering’, 2020, Vol. 16, No. 2, pp. 65 — 71.
• [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.
• [27] Schelter B., Winterhalder M., Timmer J., Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, Wiley, 2006.
• [28] Zhao Y., Chapter 7. Outlier detection, in: R and Data Mining: Examples and Case Studies, Academic Press, 2013, pp. 63 — 73.