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Time Series Data Representation and Dimensionality Reduction Techniques

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Applications of Machine Learning

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Time series data generation is a standing problem in nearly every field, such as science, business, medicine, industry, or even entertainment. As a result, there is a growing demand for analysing this data efficiently for gauging out useful information. The time series data has intrinsic features like noise, multidimensional, and large volume. When we talk about data mining, it requires a wide spectrum searching for similar patterns, such as query by content, clustering, or classification. These data mining tasks can take great help from a good and robust time series representations. It helps in the reduction of dimensions and noise adaptation and also in achieving key aspect, effectiveness, and efficiency of data processing. This chapter aims to review the basic as well as recent approaches for representations along with dimensionality reduction for time series data.

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References

  1. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Disc 15(2):107–144

    Article  MathSciNet  Google Scholar 

  2. Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Locally adaptive dimensionality reduction for indexing large time series databases. ACM Sigmod Record 30(2):151–162

    Article  Google Scholar 

  3. Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263–286

    Article  Google Scholar 

  4. Chan KP, Fu AWC (1999) Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE Int’l conference on data engineering, Sydney, Australia, 23–26 Mar 1999, pp 126–133

    Google Scholar 

  5. Shatkay H, Zdonik SB (1996) Approximate queries and representations for large data sequences. In: Proceedings of the twelfth international conference on data engineering, IEEE, pp 536–545

    Google Scholar 

  6. Karamitopoulos L, Evangelidis G (2009) A dispersion-based PAA representation for time series. WRI World Congr Comput Sci Inf Eng 4:490–494

    Google Scholar 

  7. Dan J, Shi W, Dong F, Hirota K (2013) Piecewise trend approximation: a ratio-based time series representation. In: Abstract and Applied Analysis, vol 2013. Hindawi

    Google Scholar 

  8. Schäfer P, Högqvist M (2012) SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets. In: Proceedings of the 15th international conference on extending database technology, ACM, pp 516–527

    Google Scholar 

  9. Yahyaoui H, Al-Daihani R (2019) A novel trend based SAX reduction technique for time series. Expert Syst Appl 130:113–123

    Article  Google Scholar 

  10. Lin J, Khade R, Li Y (2012) Rotation-invariant similarity in time series using bag-of-patterns representation. J Intell Inf Syst 39(2):287–315

    Article  Google Scholar 

  11. Schäfer Patrick (2015) The BOSS is concerned with time series classification in the presence of noise. Data Min Knowl Disc 29(6):1505–1530

    Article  MathSciNet  Google Scholar 

  12. Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by Shapelet transformation. Data Min Knowl Disc 28(4):851–881

    Article  MathSciNet  Google Scholar 

  13. Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. In: Proceedings of the 4th conference on foundations of data organization and algorithms, pp 69–84

    Google Scholar 

  14. Batal I, Hauskrecht M (2009) A supervised time series feature extraction technique using DCT and DWT. In: International conference on machine learning and applications, IEEE, pp 735–739

    Google Scholar 

  15. Lkhagva B, Suzuki Y, Kawagoe K (2006) New time series data representation ESAX for financial applications. In: 22nd international conference on data engineering workshops (ICDEW’06), IEEE, pp x115–x115

    Google Scholar 

  16. Schafer P (2015) Scalable time series similarity search for data analytics, Ph.d. Thesis, Humboldt University, Berlin

    Google Scholar 

  17. Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 947–956

    Google Scholar 

  18. Chan K-P, Fu AWC (1999) Efficient time series matching by wavelets. In: Proceedings 15th international conference on data engineering, IEEE, pp 126–133

    Google Scholar 

  19. Yu Y, Zhu Y, Wan D, Liu H, Zhao Q (2019) A novel symbolic aggregate approximation for time series. In: International conference on ubiquitous information management and communication, Springer, Cham, pp 805–822

    Google Scholar 

  20. Schäfer P, Leser U (2017) Fast and accurate time series classification with weasel. In: Proceedings of the ACM on conference on information and knowledge management, pp 637–646

    Google Scholar 

  21. Hatami N, Gavet Y, Debayle J (2019) Bag of recurrence patterns representation for time-series classification. Patt Anal Appl 22(3):877–887

    Article  MathSciNet  Google Scholar 

Download references

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Correspondence to Anshul Sharma .

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Sharma, A., Kumar, A., Pandey, A.K., Singh, R. (2020). Time Series Data Representation and Dimensionality Reduction Techniques. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_18

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  • DOI: https://doi.org/10.1007/978-981-15-3357-0_18

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  • Print ISBN: 978-981-15-3356-3

  • Online ISBN: 978-981-15-3357-0

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