Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

Ningning Zhang, Aijing Lin and Pengjian Shang

Physica A: Statistical Mechanics and its Applications, 2017, vol. 477, issue C, 161-173

Abstract: In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD–MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD–MKNN model has a higher forecast precision than EMD–KNN, KNN method and ARIMA.

Keywords: Ensemble empirical mode decomposition (EEMD); k-nearest neighbors (KNN); EEMD–MKNN; Forecasting; Closing price; High price (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:477:y:2017:i:c:p:161-173

DOI: 10.1016/j.physa.2017.02.072

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