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Title: Financial time series prediction: an approach using motif information and neural networks

Authors: Dadabada Pradeepkumar; Vadlamani Ravi

Addresses: Center of Excellence in Analytics, Institute for Development and Research in Banking Technology (IDRBT), Castle Hills, Masab Tank, Hyderabad, 500 057, India; School of Computer and Information Sciences (SCIS), University of Hyderabad, Hyderabad, 500 046, India ' Center of Excellence in Analytics, Institute for Development and Research in Banking Technology (IDRBT), Castle Hills, Masab Tank, Hyderabad, 500 057, India

Abstract: Financial time series prediction is an important and complex problem as well. This paper presents an approach to predict financial time series using time series motifs and artificial neural network (ANN) in tandem. A time series motif is a frequently appearing approximate pattern in a given time series. In the proposed approach, first, extreme points-clustering (EP-C) algorithm detects significant motifs. Later, ANN uses motif information to yield accurate predictions. Three ANNs namely multi-layer perceptron (MLP), general regression neural network (GRNN), and group method for data handling (GMDH) are employed. The proposed Motif+GMDH hybrid outperformed both Motif+MLP hybrid and Motif+ GRNN hybrid on three financial time series including exchange rates of both EUR/USD and INR/USD, and crude oil price (USD). Further, we compared the results of the motif-based hybrids with that of the three ANNs without motif information. We found that Motif+MLP hybrid outperformed plain MLP in all datasets statistically at 1% level of significance.

Keywords: financial time series prediction; motif; MLP; multi-layer perceptron; GRNN; general regression neural network; GMDH; group method for data handling.

DOI: 10.1504/IJDS.2020.109489

International Journal of Data Science, 2020 Vol.5 No.1, pp.79 - 109

Received: 13 Feb 2019
Accepted: 01 Jan 2020

Published online: 25 Aug 2020 *

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