Steel Price Forcasting Using Long Short-Term Memory Network Model

被引：0

作者：

Cetin, Kemal ^{[1
]}

Aksoy, Serdar ^{[2
]}

Iseri, Ismail ^{[1
]}

机构：

[1] Univ Samsun, Bolumu Ondokuz Mayis, Bilgisayar Muhendisligi, Samsun, Turkey

[2] Visionorb Ltd, Istanbul, Turkey

来源：

2019 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2019年

关键词：

Time Series; Forecasting; Long Short Term Memory Network; Artificial Intelligence; Machine Learning; Stock Price;

D O I：

10.1109/ubmk.2019.8907015

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this study, a steel price forcasting model has been developed by using the Long Short-Term Memory Network Model (LSTM) which is a customized model of recurrent neural network (RNN) architecture. The 10-years stock closing prices of the 50 largest iron and steel companies traded in the world stock exchanges and 10-years data of the scrap metal price obtained from the London metal exchange (LME) on the same day and dates combined as time series for using model training and testing stages. As a result of the forcasting made with the UKDHA model, which is designed to have 1 lstm, 7 dense layers, the best forcasting result was obtained from the forward 5-day frocasting model with the correlation coefficient R = 0.8559, MSE value 0.0026 and MAE value 0.0383.

引用

页码：612 / 617

页数：6

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