TY - CHAP

T1 - Modeling time series by means of fuzzy inference systems

AU - Pouzols, Federico Montesino

AU - Lopez, Diego R.

AU - Barros, Angel Barriga

PY - 2011

Y1 - 2011

N2 - In this chapter, we focus on long-term modeling and prediction of univariate nonlinear time series. First, a method for long-term time series prediction by means of fuzzy inference systems combined with residual variance estimation techniques is developed and validated through a number of time series prediction benchmarks. This method provides an automatic means of modeling and predicting network traffic load, and can thus be classified as a method for predictive data mining. Although the primary focus in this section is to develop a methodology for building simple and thus interpretable fuzzy inference systems, it will be shown that they also outperform some of the most accurate and commonly used techniques in the field of time series prediction.

AB - In this chapter, we focus on long-term modeling and prediction of univariate nonlinear time series. First, a method for long-term time series prediction by means of fuzzy inference systems combined with residual variance estimation techniques is developed and validated through a number of time series prediction benchmarks. This method provides an automatic means of modeling and predicting network traffic load, and can thus be classified as a method for predictive data mining. Although the primary focus in this section is to develop a methodology for building simple and thus interpretable fuzzy inference systems, it will be shown that they also outperform some of the most accurate and commonly used techniques in the field of time series prediction.

UR - http://www.scopus.com/inward/record.url?scp=79952097161&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-18084-2_2

DO - 10.1007/978-3-642-18084-2_2

M3 - Chapter

AN - SCOPUS:79952097161

SN - 9783642180835

VL - 342

T3 - Studies in Computational Intelligence

SP - 53

EP - 85

BT - Mining and Control of Network Traffic by Computational Intelligence

ER -