Financial time series prediction: an approach using motif information and neural networks
by Dadabada Pradeepkumar; Vadlamani Ravi
International Journal of Data Science (IJDS), Vol. 5, No. 1, 2020

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.

Online publication date: Thu, 10-Sep-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com