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Synergetic Artificial Intelligence and Social Robotics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

About Transformations of a Numerical Time Series Using a Linguistic Variable

Authors : Tatyana Afanasieva, Yriy Egorov, Nikolay Savinov

Published in: Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17)

Publisher: Springer International Publishing

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Abstract

Time series transforming is considered as a preprocessing stage in various data mining techniques. To obtain relevant and accurate result in time series analysis it is needed to apply relevant time series representation by suitable transformation. In the paper at the first time five transformations of a numerical time series derived on the basis of a single linguistic variable of time series values are described systematically. The formal notion of five kinds (fuzzy matrix, fuzzy vectors, fuzzy linguistic, numerical and linguistic) of time series produced by these transformations and general scheme of their computing are represented. Applications of these five representations of a numerical time series in data mining techniques are given and discussed.

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previous chapter Fuzzy Adaptive Routing in Multi-service Computer Networks under Cyber Attack Implementation
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Literature
1.
Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms (FODO 1993), pp. 69–84. Springer, London (1993)
2.
Chan, K., Fu, A.W.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering, Sydney, Australia, 23–26 March 1999, pp. 126–133 (1999)
3.
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)MATH
4.
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection a survey. ACM Comput. Surv. 41(3), 1–72 (2009). Article 15CrossRef
5.
Diday, E.: The symbolic approach in clustering, in classification and related methods. In: Bock, H.H. (ed.) North-Holland Publishing Co., Amsterdam, The Netherlands (1988)
6.
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recently proposed algorithms. Data Min. Knowl. Disc. 2016, 1–55 (2016)
7.
Lin, J., Keogh, E., Lonardi, S., Chiu, B.Y.: A symbolic representation of time series, with implications for streaming algorithms. In: DMKD 2003, pp. 2–11 (2003)
8.
9.
Song, Q., Chissom, B.: Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets Syst. 54, 1–9 (1993)CrossRef
10.
Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100, 217–228 (1998)CrossRef
11.
Perfilieva, I.G., Yarushkina, N.G., Afanasieva, T.V., Romanov, A.A.: Web-based system for enterprise performance analysis on the basis of time series data mining. In: Proceedings of the First International Scientific Conference Intelligent Information Technologies for Industry (IITI 2016). Advances in Intelligent Systems and Computing, pp. 75–86 (2016)
12.
Perfilieva, I., Yarushkina, N., Afanasieva, T., Romanov, A.: Time series analysis using soft computing methods. Int. J. Gen. Syst. 42(6), 687–705 (2013)MathSciNetCrossRefMATH
13.
14.
Afanasieva, T., Yarushkina, N., Gyskov, G.: ACL-scale as a tool for preprocessing of many-valued contexts. In: Proceedings of the Second International Workshop on Soft Computing Applications and Knowledge Discovery (SCAD 2016), pp. 2–11 (2016)
15.
Metadata
Title
About Transformations of a Numerical Time Series Using a Linguistic Variable
Authors
Tatyana Afanasieva
Yriy Egorov
Nikolay Savinov
Copyright Year
2018
Book
Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17)

Print ISBN: 978-3-319-68320-1

Electronic ISBN: 978-3-319-68321-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-68321-8_23

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