Fuzzy Approach for Detection of Anomalies in Time Series

Detecting and removing anomalies in the time series describing physical phenomena is a big challenge faced by scientists from many fields. The aim of the article is to present the assumptions of a tool for the detection of anomalies related to any issue. The proposed universal solution based on fuzzy logic and multicriteria can be applied to any time series. Through the use of aggregation of various approaches, a universal tool based on expert knowledge was obtained. The framework was tested on the energy consumption logs received from a telecommunications company and historical data from the records from the parish registers.

[1]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[2]  J. Liu,et al.  Detection of outlying data in bioavailability/bioequivalence studies. , 1991, Statistics in medicine.

[3]  R. Tsay,et al.  Outliers in multivariate time series , 2000 .

[4]  S. Strogatz.  Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering , 1995 .

[5]  John Asafu-Adjaye,et al.  The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries , 2000 .

[6]  Dimitris Kugiumtzis,et al.  Detecting Causality in Non-stationary Time Series Using Partial Symbolic Transfer Entropy: Evidence in Financial Data , 2016 .

[7]  Weiwei Chen,et al.  Anomaly detection in premise energy consumption data , 2011, 2011 IEEE Power and Energy Society General Meeting.

[8]  C. Granger,et al.  Forecasting Economic Time Series. , 1988 .

[9]  Jonathan N. Katz,et al.  Modeling Dynamics in Time-SeriesCross-Section Political Economy Data , 2011 .

[10]  Steven F. Messner,et al.  IMMIGRATION AND THE RECENT VIOLENT CRIME DROP IN THE UNITED STATES: A POOLED, CROSS‐SECTIONAL TIME‐SERIES ANALYSIS OF METROPOLITAN AREAS* , 2009 .

[11]  Clive W. J. Granger,et al.  Spectral Analysis for Economic Time Series , 1964 .

[12]  H. Tong.  Non-linear time series. A dynamical system approach , 1990 .

[13]  Ruey S. Tsay,et al.  Analysis of Financial Time Series: Tsay/Analysis of Financial Time Series , 2005 .

[14]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[15]  Richard A. DeFusco,et al.  Quantitative Investment Analysis , 2015 .

[16]  Ana Bianco,et al.  Outlier Detection in Regression Models with ARIMA Errors Using Robust Estimates , 2001 .

[17]  Hiroshi Esaki,et al.  Strip, Bind, and Search: A method for identifying abnormal energy consumption in buildings , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[18]  Stephen L Taylor,et al.  Modelling Financial Time Series , 1987 .

[19]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[20]  Marimuthu Palaniswami,et al.  Anomaly detection in wireless sensor networks , 2008, IEEE Wireless Communications.

[21]  Oliver Niggemann,et al.  A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[22]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[23]  Adam Kiersztyn,et al.  Multi-Criteria Decision-Making System for Detecting Anomalies in the Electrical Energy Consumption of Telecommunication Facilities , 2019, DEStech Transactions on Computer Science and Engineering.

[24]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[25]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[26]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[27]  James D. Hamilton.  Time Series Analysis , 1994 .

[28]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[29]  Robert A. M. Gregson,et al.  Time series in psychology , 1983 .