The Application of Random Noise Reduction By Nearest Neighbor Method To Forecasting of Economic Time Series

• Authors: Monika Miśkiewicz-Nawrocka
• Languages of publication: EN

Abstracts

EN

Since the deterministic chaos appeared in the literature, we have observed a huge increase in interest in nonlinear dynamic systems theory among researchers, which has led to the creation of new methods of time series prediction, e.g. the largest Lyapunov exponent method and the nearest neighbor method. Real time series are usually disturbed by random noise, which can complicate the problem of forecasting of time series. Since the presence of noise in the data can significantly affect the quality of forecasts, the aim of the paper will be to evaluate the accuracy of predicting the time series filtered using the nearest neighbor method. The test will be conducted on the basis of selected financial time series.

Keywords

EN

random noise reduction; nearest neighbor method; largest Lyapunov exponent; financial time series forecasting; C01; C53; G17

• Publisher: Sciendo
• Journal: Folia Oeconomica Stetinensia
• Year: 2014
• Volume: 13
• Issue: 2
• Pages: 96-108

Dates

received

2013-10-20

accepted

2014-01-17

online

2014-07-08

Contributors

author

Monika Miśkiewicz-Nawrocka

• University of Economics in Katowice, Faculty of Management, Department of Mathematics, 1 Maja 50, 40-287 Katowice, Poland

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Identifiers

DOI

10.2478/foli-2013-0020