Abstract
In multivariate time series (MTS) data, the formation of high-quality, reliable, and statistically sound information by analyzing and interpreting large data set is becoming a challenging task due to its increased complexity and over-fitting problems. Preprocessing steps play an important role in overcoming the performance issues of MTS data analysis. Feature and data subset selections are important preprocessing steps before applying any data mining functionalities like clustering and classification to identify the efficient and valuable predictors and relevant instances that better represents the underlying process of the data. Here we introduced an optimized preprocessing step using process control charts to extract a subset of key instances that form the representative set of the core group and utilized two classification algorithms to analyze the performance. The results are also compared for different test scenarios by adding standard dimensionality reduction methods and numerosity reduction approaches.
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
Box, G.E., Jenkins, G.M., Reinsel, G.C.: Multivariate time series analysis. In: Time Series Analysis, 4th Edn., pp. 551–595 (2008)
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)
Tiao, G.C., Box, G.E.: Modeling multiple time series with applications. J. Am. Stat. Assoc. 76(376), 802–816 (1981)
Geurts, P.: Pattern extraction for time series classification. In: PKDD, vol. 1, pp. 115–127, Sept., 2001
Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley (2013)
Peña, D., Poncela, P.: Dimension reduction in multivariate time series. In: Advances in Distribution Theory, Order Statistics, and Inference, pp. 433–458 (2006)
Spiegel, S., Gaebler, J., Lommatzsch, A., De Luca, E., Albayrak, S.: Pattern recognition and classification for multivariate time series. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, pp. 34–42, Aug., 2011. ACM
Guerrero-Mosquera, C., Verleysen, M., Vazquez, A.N.: EEG feature selection using mutual information and support vector machine: a comparative analysis. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4946–4949, Aug. 2010. IEEE
Hallac, D., Nystrup, P., Boyd, S.: Greedy Gaussian segmentation of multivariate time series (2016). arXiv:1610.07435
Hamilton, J.D.: Time series analysis, vol. 2. Princeton University Press, Princeton (1994)
Maćkiewicz, A., Ratajczak, W.: Principal components analysis (PCA). Comput. Geosci. 19(3), 303–342 (1993)
Nanditha, J., Sruthi, K.N., Ashok, S., Judy, M.V.: Optimized defect prediction model using statistical process control and correlation-based feature selection method. In: Intelligent Systems Technologies and Applications, pp. 355–366. Springer, Cham (2016)
Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)
Leavenworth, R.S., Grant, E.L.: Statistical Quality Control. Tata McGraw-Hill Education (2000)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)
Suma, V.R., Renjith, S., Ashok, S., Judy, M.V.: Analytical study of selected classification algorithms for clinical dataset. Indian J. Sci. Technol. 9(11) (2016)
Lewis, D.D.: Naive (Bayes) at forty: the independence assumption in information retrieval. In: European Conference on Machine Learning, pp. 4–15, Apr, 1998. Springer, Berlin, Heidelberg
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345, San Mateo. Morgan Kaufmann (1995)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press (2002)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
John, J., Ashok, S. (2019). Process Framework for Modeling Multivariate Time Series Data. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_56
Download citation
DOI: https://doi.org/10.1007/978-981-13-0514-6_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0513-9
Online ISBN: 978-981-13-0514-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)