人工神经网络
系列(地层学)
时间序列
计算机科学
主题(音乐)
人工智能
数据挖掘
财务
作者
Dadabada Pradeepkumar,Vadlamani Ravi
出处
期刊:International journal of data science
[Inderscience Enterprises Ltd.]
日期:2020-08-25
卷期号:5 (1): 79-79
标识
DOI:10.1504/ijds.2020.10031614
摘要
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.
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