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Time Series Similarity Search Methods for Sensor Data

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Abstract

Time series is a type of dynamic data used in many applications. Time series speed may vary from milliseconds to years or decades. In past decade, rise in various sensor based technologies have made time series sensor data available easily and in larger extent. Therefore, high dimensionality of the data in customized applications is always a challenging task for efficient mathematical computing accuracy and performance optimization. One of the major operations performed on time series is finding out similarity between two or more time series. Two time series can be considered similar on the basis of distance between them. Computation of these distances is achieved by various methods. This research study aims to compare eight such methods for accelerometer sensor data collected from smartphone based accelerometer during car and scooter ride. This study also proposes a modified method of distance computation considering tyre pressure and weight of the vehicle. Research findings have shown that modified method of DTW (dynamic time warping) is proved more efficient in distinguishing time series generated by two different weights’ vehicles. Results have shown as maximum of 67% recognition rate is achieved by modified DTW method compared to traditional DTW method.

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REFERENCES

  1. Montero, P. and Vilar, J.A., TSclust: An R Package for time series clustering, J. Stat. Software, 2014, vol. 62, no. 1, pp. 1–43.  https://doi.org/10.18637/jss.v062.i01

    Article  Google Scholar 

  2. Bennett, R., Spatial Time Series: Analysis–Forecasting–Control, London: Pion, 1979.

    MATH  Google Scholar 

  3. Wei, W.S., Time Series Analysis: Univariate and Multivariate Methods, Reading, Mass.: Addison-Wesley, 1989.

    Google Scholar 

  4. Kalpakis, K., Gada, D., and Puttagunta, V., Distance measures for effective clustering of ARIMA time-series, Proc. 2001 IEEE Int. Conf. on Data Mining, San Jose, Calif., 2001, IEEE, 2001, pp. 273–280.  https://doi.org/10.1109/ICDM.2001.989529

  5. Ahonen, T.E., Lemström, K., and Linkola, S., Compression-based similarity measures in symbolic, polyphonic music, Proc. 12th Int. Society for Music Information Retrieval Conf. (ISMIR 2011), Klapuri, A. and Leider, C., Eds., Miami: Univ. Miami, 2011, pp. 91–96.

  6. Keogh, S., Lonardi, S., and Ratanamahatana, C.A., Towards parameter-free data mining, Proc. Tenth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Seattle, Wash., 2004, New York: Association for Computing Machinery, 2004, pp. 206–215.  https://doi.org/10.1145/1014052.1014077

  7. Chouakria, A.D. and Nagabhushan, P.N., Adaptive dissimilarity index for measuring time series proximity, Adv. Data Anal. Classification, 2007, vol. 1, no. 1, pp. 5–21.  https://doi.org/10.1007/s11634-006-0004-6

    Article  MathSciNet  MATH  Google Scholar 

  8. Myers, C. and Rabiner, L., A level building dynamic time warping algorithm for connected word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1981, vol. 29, no. 2, pp. 284–297.  https://doi.org/10.1109/TASSP.1981.1163527

    Article  MATH  Google Scholar 

  9. Myers, C., Rabiner, L., and Rosenberg, A., Performance tradeoffs in dynamic time warping algorithms for isolated word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1980, vol. 28, no. 6, pp. 623–635.  https://doi.org/10.1109/TASSP.1980.1163491

    Article  MATH  Google Scholar 

  10. Rabiner, L., Rosenberg, A., and Levinson, S., Considerations in dynamic time warping algorithms for discrete word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1978, vol. 26, no. 6, pp. 575–582.  https://doi.org/10.1109/TASSP.1978.1163164

    Article  MATH  Google Scholar 

  11. Zhang, N. and Yao, Y., Speaker recognition based on dynamic time warping and Gaussian mixture model, 39th Chinese Control Conf. (CCC), Shenyang, China, 2020, IEEE, 2020, pp. 1174–1177.  https://doi.org/10.23919/CCC50068.2020.9188632

  12. Jing, M., Mac Namee, B., McLaughlin, D., Steele, D., McNamee, S., Cullen, P., Finlay, D., and McLaughlin, J., Enhance categorisation of multilevel high-sensitivity cardiovascular biomarkers from lateral flow immunoassay images via neural networks and dynamic time warping, IEEE Int. Conf. on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, IEEE, 2020, pp. 365–369. https://doi.org/10.1109/ICIP40778.2020.9190827

  13. Huang, G., Chen, X., and Chen, Y., P-P and dynamic time warped P-SV wave AVA joint-inversion with l 1–2 regularization, IEEE Trans. Geosci. Remote Sensing, 2021, vol. 59, no. 7, pp. 5535–5548.  https://doi.org/10.1109/TGRS.2020.3022051

    Article  Google Scholar 

  14. Lathe, A.S. and Gautam, A., Estimating vertical profile irregularities from vehicle dynamics measurements, IEEE Sensors J., 2020, vol. 20, no. 1, pp. 377–385.  https://doi.org/10.1109/JSEN.2019.2942317

    Article  Google Scholar 

  15. Chen, Z. and Gu, J., High-throughput dynamic time warping accelerator for time-series classification with pipelined mixed-signal time-domain computing, IEEE J. Solid-State Circuits, 2021, vol. 56, no. 2, pp. 624–635. https://doi.org/10.1109/JSSC.2020.3021066

    Article  Google Scholar 

  16. Si, Y., Chen, Z., Sun, J., Zhang, D., and Qian, P., A data-driven fault detection framework using Mahalanobis distance based dynamic time warping, IEEE Access, 2020, vol. 8, pp. 108359–108370.  https://doi.org/10.1109/ACCESS.2020.3001379

    Article  Google Scholar 

  17. Wang, H., Huo, N., Li, J., Wang, K., and Wang, Z., A road quality detection method based on the Mahalanobis–Taguchi system, IEEE Access, 2018, vol. 6, pp. 29078–29087.  https://doi.org/10.1109/ACCESS.2018.2839765

    Article  Google Scholar 

  18. Geler, Z., Kurbalija, V., Ivanović, M., and Radovanović, M., Time-series classification with constrained DTW distance and inverse-square weighted k-NN, Int. Conf. on Innovations in Intelligent Systems and Applications (INISTA), Novi Sad, Serbia, 2020, IEEE, 2020, pp. 1–7.  https://doi.org/10.1109/INISTA49547.2020.9194639

  19. Xie, L., Chen, P., Chen, S., Yu, K., and Sun, H., Low-cost and highly sensitive wearable sensor based on Napkin for health monitoring, Sensors, 2019, vol. 19, no. 15, p. 3427.  https://doi.org/10.3390/s19153427

    Article  Google Scholar 

  20. Liu, Y., Nie, L., Han, L., Zhang, L., and Rosenblum, D.S., Action2Activity: Recognizing complex activities from sensor data, Proc. Twenty-Fourth Int. Joint Conf. on Artificial Intelligence (IJCAI 2015), Buenos Aires, 2015, AAAI Press, 2015, pp. 1617–1623.

  21. Liu, Y., Nie, L., Liu, L., and Rosenblum, D.S., From action to activity: Sensor-based activity recognition, Neurocomputing, 2016, vol. 181, pp. 108–115.  https://doi.org/10.1016/j.neucom.2015.08.096

    Article  Google Scholar 

  22. Rahman, M.Z.U., Shaik, R.A., and Reddy, D.V.R.K., Efficient and simplified adaptive noise cancelers for ECG sensor based remote health monitoring, IEEE Sensors J., 2012, vol. 12, no. 3, pp. 566–573.  https://doi.org/10.1109/JSEN.2011.2111453

    Article  Google Scholar 

  23. Echoda, N.J.A., Farooq, S.Z., Xuebao, H., Chukwuma, J., and Yang, D., Multipath mitigation analysis using Hatch filter and DTW in single frequency RTK, IEEE Int. Conf. on Artificial Intelligence and Information Systems (ICAAIS), Dalian, China, 2020, IEEE, 2020, pp. 273–277.  https://doi.org/10.1109/ICAIIS49377.2020.9194831

  24. Hong, F., Chen, J., Zhang, Z., Wang, R., and Gao, M., Time series risk prediction based on LSTM and a variant DTW algorithm: Application of bed inventory overturn prevention in a pant-leg CFB boiler, IEEE Access, 2020, vol. 8, pp. 156634–156644.  https://doi.org/10.1109/ACCESS.2020.3009679

    Article  Google Scholar 

  25. Ashouri, A., Hu, Y., Newsham, G.R., and Shen, W., Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation, IEEE 14th Int. Conf. on Automation Science and Engineering (CASE), Munich, 2018, IEEE, 2018, pp. 1400–1405.  https://doi.org/10.1109/COASE.2018.8560433

  26. Basavaraju, A., Du, J., Zhou, F., and Ji, J., A machine learning approach to road surface anomaly assessment using smartphone sensors, IEEE Sensors J., 2020, vol. 20, no. 5, pp. 2635–2647.  https://doi.org/10.1109/JSEN.2019.2952857

    Article  Google Scholar 

  27. Jeong, Y.-S., Jeong, M.K., and Omitaomu, O.A., Weighted dynamic time warping for time series classification, Pattern Recognit., 2011, vol. 44, no. 9, pp. 2231–2240.  https://doi.org/10.1016/j.patcog.2010.09.022

    Article  Google Scholar 

  28. Wang, L., Wang, B., Han, Z., Zhang, Z., Kong, F., and Wang, L., Research of the hybrid tire pressure monitoring system, DEStech Trans. Eng. Technol. Res., 2017, pp. 191–194.  https://doi.org/10.12783/dtetr/mime2016/10230

  29. Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., and Selavo, L., Real time pothole detection using Android smartphones with accelerometers, Int. Conf. on Distributed Computing in Sensor Systems and Workshops (DCOSS), Barcelona, 2011, IEEE, 2011, pp. 1–6.  https://doi.org/10.1109/DCOSS.2011.5982206

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Funding

This research work mentioned in this paper is outcome of the project work undertaken by authors under the Vishvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India. We are thankful for the support extended to us.

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  1. P.G. Department of Computer Science, SNDT University, 400047, Mumbai, Maharashtra, India

    Anupama Jawale &  Ganesh Magar

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  1. Anupama Jawale
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Correspondence to Anupama Jawale or Ganesh Magar.

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Anupama Jawale, Ganesh Magar Time Series Similarity Search Methods for Sensor Data. Aut. Control Comp. Sci. 56, 120–129 (2022). https://doi.org/10.3103/S0146411622020067

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