Stock Price Forecast using Wavelet Transoformations in Multiple Time Windows and Neural Networks

被引：0

作者：

Kulaglic, Ajla ^{[1
]}

Ustundag, Burak Berk ^{[1
]}

机构：

[1] Istanbul Tech Univ, Grad Sch Sci Engn & Technol, Istanbul, Turkey

来源：

2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2018年

关键词：

discrete wavelet transfirm; neural networks; stock price forecasting; Apple Stock Prices; financial time series data; SERIES; MODEL;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a highly reliable and accurate stock-price prediction model. We aim to anticipate the stock price with respect to multiple patterns in different time scales. The stock price time-series are decomposed, using discrete wavelet transform (DWT), into temporal resolution of varying scales. Then, each subseries is used to predict the stock price using two types of neural network (NN) models with one and two hidden layers. Results show that having multiple time windows in input datasets together with DWT decrease the RMSE of NN models below 10%.

引用

页码：518 / 521

页数：4

共 18 条

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