Abstract
This paper evaluates the similarity between two time series generated by two sensors manufactured by different companies, trying to provide some valuable information upon choosing sensors of different brands. Spearman correlation coefficient analysis and Euclidean distance measurement have been applied. Experiment is carried out on R. Visualization of the studied time series and results of similarity measured over time series by Spearman correlation coefficient and Euclidean distance are presented. Besides, the consistency and inconsistency in the analysis results of two measurements have been discussed in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
de Jong, P.: Time series analysis. In: Predictive Modeling Applications in Actuarial Science. Predictive Modeling Techniques, vol. 1, p. 427 (2014)
Reimche, W., Südmersen, U., Pietsch, O., et al.: Basics of vibration monitoring for fault detection and process control. In: 3rd Pan-American Conference for Non-Destructive Testing. Rio de Janeiro, Brazil (2003)
Serrà, J., Arcos, J.L.: An empirical evaluation of similarity measures for time series classification. Knowl.-Based Syst. (2014)
Ding, H., Trajcevski, G., Scheuermann, P., et al.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)
Liao, H., Xu, Z., Zeng, X.J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf. Sci. 271, 125–142 (2014)
Gunopulos, D., Das, G.: Time series similarity measures (tutorial PM-2). In: Tutorial Notes of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–307. ACM (2000)
Batista, G.E., Keogh, E.J., Tataw, O.M., et al.: CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Disc. 28(3), 634–669 (2014)
Gautheir, T.D.: Detecting trends using spearman’s rank correlation coefficient. Environ. Forensics 2(4), 359–362 (2001)
Mudelsee, M.: Estimating Pearson’s correlation coefficient with bootstrap confidence interval from serially dependent time series. Math. Geol. 35(6), 651–665 (2003)
Seneviratne, S.I., Donat, M.G., Mueller, B., et al.: No pause in the increase of hot temperature extremes. Nat. Clim. Change 4(3), 161–163 (2014)
Pearson’s. Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients… (2007)
Breu, H., Gil, J., Kirkpatrick, D., et al.: Linear time Euclidean distance transform algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 529–533 (1995)
Mori, U., Mendiburu, A., Lozano, J.A.: Distance measures for time series in R: the TSdist package
Da Silva, R., Stasiu, R., Orengo, V.M., et al.: Measuring quality of similarity functions in approximate data matching. J. Informetrics 1(1), 35–46 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ye, J., Xiao, C., Esteves, R.M., Rong, C. (2015). Time Series Similarity Evaluation Based on Spearman’s Correlation Coefficients and Distance Measures. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-28430-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28429-3
Online ISBN: 978-3-319-28430-9
eBook Packages: Computer ScienceComputer Science (R0)