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International Journal Of Chemistry, Mathematics And Physics(IJCMP)

A Method for Detection of Outliers in Time Series Data

Evan Abdulmajeed Hasan


International Journal of Chemistry, Mathematics And Physics(IJCMP), Vol-3,Issue-3, May - June 2019, Pages 56-66 , 10.22161/ijcmp.3.3.2

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An outlier is a data value that is an unusually small or large, or that deviates from the pattern of the rest of the data. Outliers are usually removed from the data set before fitting a forecasting model, or not removed but the forecasting model adjusted in presence of outliers. There are four types of OUTLIERS are as follows: Additive outlier (AO), Innovational outlier (IO), Level shift (LS) and Temporary change (TC). There is more than one method for the detection of outlier; the study considers the detection of outlier in two cases: first, at the time when the parameters are known. Second, when the parameters are unknown. There are several reasons for outlier detection and adjustment in time series analysis and forecasting which are mentioned in this study. The study has used the volume of water inflow in the reservoir of Dokan dam in Sulaymaniah city as a time series for the purpose of the study. The study came to conclude that throughout the research, the following conclusions: first, every time increasing the critical value, the value of residual standard error (with outlier adjustment) increased. Second, every time increasing the critical value, the number of outlier values decreased. Third, in the case of the presence of outliers the forecasts with adjustment of outliers better than the forecasts without adjusting outliers.

ARMA model, Innovational Outlier, Temporary Change, Time Series

[1] Ahmad, S., & Purdy, S. (2016). Real-time anomaly detection for streaming analytics. arXiv preprint arXiv:1607.02480.
[2] Aminikhanghahi, S., & Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and information systems, 51(2), 339-367.
[3] Arumugam, P., & Saranya, R. (2018). Outlier Detection and Missing Value in Seasonal ARIMA Model Using Rainfall Data. Materials Today: Proceedings, 5(1), 1791-1799.
[4] Cabrieto, J., Tuerlinckx, F., Kuppens, P., Grassmann, M., &Ceulemans, E. (2017). Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Behavior
[5] Capozzoli, A., Lauro, F., & Khan, I. (2015). Fault detection analysis using data mining techniques for a cluster of smart office buildings. Expert Systems with Applications, 42(9), 4324-4338.
[6] Chen, W., Zhou, K., Yang, S., & Wu, C. (2017). Data quality of electricity consumption data in a smart grid environment. Renewable and Sustainable Energy Reviews, 75, 98-105.
[7] Filonov, P., Lavrentyev, A., &Vorontsov, A. (2016). Multivariate industrial time series with cyber-attack simulation: Fault detection using anlstm-based predictive data model. arXiv preprint arXiv:1612.06676.
[8] Frantz, D., Röder, A., Udelhoven, T., & Schmidt, M. (2015). Enhancing the detectability of clouds and their shadows in multitemporal dryland Landsat imagery: Extending Fmask. IEEE Geoscience and Remote Sensing Letters, 12(6), 1242-1246.
[9] Ganz, F., Puschmann, D., Barnaghi, P., &Carrez, F. (2015). A practical evaluation of information processing and abstraction techniques for the internet of things. IEEE Internet of Things journal, 2(4), 340-354.
[10] Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., & Hobart, G. W. (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158, 220-234.
[11] Johansen, S., & Nielsen, B. (2016). Asymptotic theory of outlier detection algorithms for linear time series regression models. Scandinavian Journal of Statistics, 43(2), 321-348.
[12] Kontaki, M., Gounaris, A., Papadopoulos, A. N., Tsichlas, K., &Manolopoulos, Y. (2016). Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Information systems, 55, 37-53.
[13] Li, L., Das, S., John Hansman, R., Palacios, R., & Srivastava, A. N. (2015). Analysis of flight data using clustering techniques for detecting abnormal operations. Journal of Aerospace information systems, 12(9), 587-598.
[14] Liu, M., Shi, J., Cao, K., Zhu, J., & Liu, S. (2018). Analyzing the training processes of deep generative models. IEEE transactions on visualization and computer graphics, 24(1), 77-87.
[15] Liu, S., Wright, A., &Hauskrecht, M. (2018). Change-point detection method for clinical decision support system rule monitoring. Artificial intelligence in medicine, 91, 49-56.
[16] Liu, Z., Verstraete, M. M., & de Jager, G. (2018). Handling outliers in model inversion studies: a remote sensing case study using MISR-HR data in South Africa. South African Geographical Journal, 100(1), 122-139.
[17] Loureiro, D., Amado, C., Martins, A., Vitorino, D., Mamade, A., & Coelho, S. T. (2016). Water distribution systems flow monitoring and anomalous event detection: A practical approach. Urban Water Journal, 13(3), 242-252.
[18] Martí, L., Sanchez-Pi, N., Molina, J., & Garcia, A. (2015). Anomaly detection based on sensor data in petroleum industry applications. Sensors, 15(2), 2774-2797.
[19] Reiche, J., Verbesselt, J., Hoekman, D., & Herold, M. (2015). Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment, 156, 276-293.
[20] Rousseeuw, P. J., &Bossche, W. V. D. (2018). Detecting deviating data cells. Technometrics, 60(2), 135-145.
[21] Rousseeuw, P., Perrotta, D., Riani, M., & Hubert, M. (2019). Robust monitoring of time series with application to fraud detection. Econometrics and statistics, 9, 108-121.
[22] Sprint, G., Cook, D. J., &Schmitter-Edgecombe, M. (2016). Unsupervised detection and analysis of changes in everyday physical activity data. Journal of biomedical informatics, 63, 54-65.
[23] Sprint, G., Cook, D. J., Fritz, R., &Schmitter-Edgecombe, M. (2016). Using smart homes to detect and analyze health events. Computer, 49(11), 29-37.
[24] Staal, O. M., Sælid, S., Fougner, A., &Stavdahl, Ø. (2019). Kalman smoothing for objective and automatic preprocessing of glucose data. IEEE journal of biomedical and health informatics, 23(1), 218-226.
[25] Stumpf, A., Malet, J. P., &Delacourt, C. (2017). Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides. Remote sensing of environment, 189, 40-55.
[26] Wang, B., & Mao, Z. (2018). Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data. Measurement and Control, 51(3-4), 83-93.
[27] Zhang, Q., Pandey, B., &Seto, K. C. (2016). A robust method to generate a consistent time series from DMSP/OLS nighttime light data. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5821-5831.