Abstract
A pre-processing step to reduce the volume of data but suffer an acceptable loss of data quality before applying data mining algorithms on time series data is needed to decrease the input data size. Input size reduction is an important step in optimizing time series processing, e.g. in data mining computations. During the last two decades various time series dimensionality reduction techniques have been proposed. However no study has been dedicated to gauge these time series dimensionality reduction techniques in terms of their effectiveness of producing a reduced representation of the input time series that when applied to various data mining algorithms produces good quality results. In this paper empirical evidence is given by comparing three reduction techniques on various data sets and applying their output to four different data mining algorithms. The results show that it is sometimes feasible to use these techniques instead of using the original time series data. The comparison is evaluated by running data mining methods over the original and reduced sets of data. It is shown that one dimensionality reduction technique managed to generate results of over 83% average accuracy when compared to its benchmark results.
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References
Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Discov. 7(4), 349–371 (2003)
Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1–34 (2012)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)
Agrawal, Rakesh, Faloutsos, Christos, Swami, Arun: Efficient similarity search in sequence databases. In: Lomet, David B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-57301-1_5
Chan, K., Fu, A.W.-c.: Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering, ICDE 1999, Washington DC (1999)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)
Vlachos, M., Gunopulos, D.: Indexing time-series under conditions of noise. In: Data Mining in Time Series Databases, pp. 67–100. World Scientific Press (2004)
Struzik, Z., Siebes, A.: Measuring time series similarity through large singular features revealed with wavelet transformation. In: Proceedings of the 10th International Workshop on Database and Expert System Applications (1999)
Megalooikonomou, V., Li, G., Wang, Q.: A dimensionality reduction technique for efficient similarity analysis of time series databases. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, CIKM 2004, Washington DC (2004)
Chakrabarti, K., Keogh, E., Mehrotra, S., Pazzani, M.: Locally Adaptive Dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. (TODS), pp. 188–228 (2002)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107–144 (2007). https://doi.org/10.1007/s10618-007-0064-z
Bode, G., Schreiber, T., Baranski, M., Müller, D.: A time series clustering approach for Building Automation and Control Systems. Appl. Energy 238, 1337–1345 (2019)
Caiado, J., Crato, N., Poncela, P.: A fragmented-periodogram approach for clustering big data time series. Adv. Data Anal. Classif. 14, 117–146 (2020)
Wismuller, A., et al.: Cluster analysis of biomedical image time-series. Int. J. Comput. Vis. 46(2), 103–128 (2002)
Luo, W., Gallagher, M., Wiles, J.: Parameter-free search of time-series discord. J. Comput. Sci. Technol. 28(2), 300–310 (2013)
Chuah, M.C., Fu, F.: ECG anomaly detection via time series analysis. In: Thulasiraman, Parimala, He, Xubin, Xu, Tony Li, Denko, Mieso K., Thulasiram, Ruppa K., Yang, Laurence T. (eds.) ISPA 2007. LNCS, vol. 4743, pp. 123–135. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74767-3_14
Keogh, E., Lin, J., Fu, A.W., Van Herle, H.: Finding unusual medical time-series subsequences: algorithms and applications. IEEE Trans. Inf Technol. Biomed. 10, 429–439 (2006)
Wei, L., Keogh, E., Xi, X.: SAXually explicit images: finding unusual shapes. In: Sixth International Conference on Data Mining, 2006, ICDM 2006, Hong Kong (2007)
Yi, B., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of the 26th International Conference on Very Large Databases, San Francisco, VLDB 2000 (2000)
Chaovalit, P., Gangopadhyay, A., Karabatis, G., Chen, Z.: Discrete wavelet transform-based time series analysis and mining. ACM Comput. Surv. (CSUR) 43(12), 1–37 (2011)
Gunopulos, D.: Tutorial Slides: Dimensionality Reduction Techniques (2001)
Rand, W.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846–850 (1971)
Fowlkes, E., Mallows, C.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78, 553–569 (1983)
Alcala-Fdez, J., et al.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17, 255–287 (2011)
Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2010). http://archive.ics.uci.edu/ml
Fonollosa, J., Sheik, S., Huerta, R., Marco, S.: Reservoir Computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring. Sens. Actuators B: Chem. 215, 618–629 (2015)
Chen, Y., et al.: The UCR Time Series Classification Archive, July 2015. http://www.cs.ucr.edu/~eamonn/time_series_data/
Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM 2005, Washington (2005)
Bahadori, S., Charkari, N.M.: Increasing efficiency of time series clustering by dimension reduction techniques (2018)
Sirisambhand, K., Ratanamahatana, C.H.: A dimensionality reduction technique for time series classification using additive representation. In: Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, Singapore (2019)
Wang, Lin, Lu, Faming, Cui, Minghao, Bao, Yunxia: Survey of methods for time series symbolic aggregate approximation. In: Cheng, Xiaohui, Jing, Weipeng, Song, Xianhua, Lu, Zeguang (eds.) ICPCSEE 2019. CCIS, vol. 1058, pp. 645–657. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-0118-0_50
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Borg, J., Vella, J.G. (2020). Mining Massive Time Series Data: With Dimensionality Reduction Techniques. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_45
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