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Method for Detecting and Eliminating Data Time Series Outlier in High-Speed Process Data Sensors

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The objective is to identify and eliminate measurement errors in data time series of data sensors of high-speed processes. Fractal analysis is used to identify and eliminate outliers in data time series of data sensors of high-speed processes. The proposed method provides an accurate detection of an outlier and an acceptable error in a series characteristic modification when the outlier is replaced with a predicted value. As a result, the time series becomes persistent again. The originality of the method consists in using such a hidden regularity of time series as a correlation of values to detect measurement errors. The importance of the study is determined by the immediacy of the problem of detecting and eliminating outliers in data time series of data sensors with stringent requirements imposed upon to reduce decision-making time in the event of critical situations.
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High-Speed Processes; Measurement Errors; Outliers; Persistence

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L.W.P. Biscainho, Random Signals and Stochastic Processes, Academic Press Library in Signal Processing, Volume. 1. Signal Processing Theory and Machine Learning. Edited by Diniz, P. S.R.; Suykens, J.A.K.; Chellappa R.; Theodoridis, S. Chapter 4, 2014, pp.113-168.

M.R. Maleki, A.Amiri,; Castagliola, P. Measurement errors in statistical process monitoring: A literature review. Computers & Industrial Engineering, 103, (2017), pp. 316-329.

Siegel E. Practical Business Statistics. Academic Press; 6 edition. 2011.

Efstratiadis, A., Dialynas, Y.G., Kozanis, S., Koutsoyiannis, D. A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence. Environmental Modelling & Software, 62(2014), pp. 139-152.

Dave D., Varma T. A Review of various statistical methods for Outlier Detection. International Journal of Computer Science & Engineering Technology, 5, 02(2014), pp. 137–140.

URL: (12/02/2016)

de Oliveira, E.C., de Faro Orlando, A., dos Santos Ferreira, A.L., de Oliveira Chaves, C.E. Comparison of different approaches for detection and treatment of outliers in meter proving factors determination. Flow Measurement and Instrumentation, 48(2016), pp. 29-35.

Zhu, Y., Ting, K.M., Carman, M.J. Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition, 60(2016), pp. 983-997.

Qi, M., Fu, Z., Chen, F. Outliers detection method of multiple measuring points of parameters in power plant units. Applied Thermal Engineering, 85(2015), pp. 297-303.

Yuen, K.-V.; Ortiz, G.A. Outlier detection and robust regression for correlated data. Computer Methods in Applied Mechanics and Engineering, 313(2017), pp. 632-646.

Sangeux, M., Polak, J. A simple method to choose the most representative stride and detect outliers. Gait & Posture, 41, 2(2015), pp. 726-730.

Zhang, L., Wang, D., Gao, R., Li, P., Zhang, W., Mao, J., Yu, L., Ding, X., Zhang, Q. Improvement on enhanced Monte-Carlo outlier detection method. Chemometrics and Intelligent Laboratory Systems, 151(2016), pp. 89-94.

Aljoumani, B., Sànchez-Espigares, J.A., Cañameras, N., Josa, R., Monserrat, J. Time series outlier and intervention analysis: Irrigation management influences on soil water content in silty loam soil. Agricultural Water Management, 111(2012), pp. 105-114.

Guo, J., Huang, W., Williams, B.M. Real time traffic flow outlier detection using short-term traffic conditional variance prediction. Transportation Research Part C: Emerging Technologies, 50(2015), pp. 160-172.

Marczak, M., Proietti, T. Outlier detection in structural time series models: The indicator saturation approach. International Journal of Forecasting, 32, 1(2016), pp. 180-202.

Tang, B., He, H. A Local Density-Based Approach for Outlier Detection. Neurocomputing, 241(2017), 171-180.

Huang, J., Zhu, Q., Yang, L., Cheng, D.D., Wu, Q. A novel outlier cluster detection algorithm without top-n parameter. Knowledge-Based Systems, 121(2017), pp. 32-40.

Ha, J., Seok, S., Lee, J.-S. A precise ranking method for outlier detection. Information Sciences, 324, 10(2015), pp. 88-107.

Bhattacharya, G., Ghosh, K., Chowdhury, A.S. Outlier detection using neighborhood rank difference. Pattern Recognition Letters, 60-61(2015), pp. 24-31.

Pasillas-Díaz, J.R., Ratté, S. An Unsupervised Approach for Combining Scores of Outlier Detection Techniques, Based on Similarity Measures. Electronic Notes in Theoretical Computer Science. 329(2016), pp. 61-77.

Gunupudi, R.K., Nimmala, M., Gugulothu, N., Gali, S.R CLAPP: A self-constructing feature clustering approach for anomaly detection. Future Generation Computer Systems, In Press, Corrected Proof, Available online 4 January 2017.

Huang, J., Zhu, Q., Yang, L., Feng, J. A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems, 92(2016), pp. 71-77.

Maciá-Pérez, F., Berna-Martinez, J.V., Oliva, A.F., Ortega, M.A.A. Algorithm for the detection of outliers based on the theory of rough sets. Decision Support Systems, 75(2015), pp. 63-75.

Chiang, A., David, E., Lee, Y.-J., Leshem, G., Yeh, Y.-R. A study on anomaly detection ensembles. Journal of Applied Logic, 21(2017), pp. 1-13.

Gil, P., Martins H., Januário, F. Detection and accommodation of outliers in Wireless Sensor Networks within a multi-agent framework. Applied Soft Computing, 42(2016), pp. 204–214.

Saka M.H. Performance evaluation of outlier detection methods in GNSS vector networks using 1D and 3D component analysis. Measurement, 82(2016), pp. 145-150.

Zhang, L., Lin, J., Karim, R. An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection. Reliability Engineering & System Safety, 142(2015), pp. 482-497.

GOST R ISO 5479-2002. Statistical methods. Test for departure of the probability distribution from the normal distribution. 2002.

Peters, E.E. Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility. (WILEY FINANCE). Moscow: Mir, 2000.

Shelukhin, O.I. Multifractals. Infocommunication applications. Moscow: Goryachaya Liniya – Telekom, 2011.

Vaclavovic, P., Dousa, J. Backward smoothing for precise GNSS applications. Advances in Space Research. 56, 8, (2015), pp. 1627-1634.

Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74(2017), pp. 902-924.

Pylkin, A.N., Demidova, L.A., Skvortsov, S.V., Skvortsova, T.S. Hybrid models for forecasting short time series. Moscow: Horline-Telecom; 2012.

Gupta, P., Batra, S.S., Jayadeva. Sparse short-term time series forecasting models via minimum model complexity. Neurocomputing, 243(2017), pp. 1-11.

Sadaei, H.J., Guimarães, F.G., da Silva, C.J., Lee, M.H., Eslami, T. Short-term load forecasting method based on fuzzy time series, seasonality and long memory process. International Journal of Approximate Reasoning, 83(2017), pp. 196-217.


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