Author/Authors
yiğit, tuncay süleyman demirel üniversitesi - mühendislik fakültesi - bilgisayar mühendisliği bölümü, Isparta, Türkiye , aksoy, bekir isparta uygulamalı bilimler üniversitesi - teknoloji fakültesi - mekatronik mühendisliği bölümü, Isparta, Türkiye , ersoy, mevlüt süleyman demirel üniversitesi - mühendislik fakültesi - bilgisayar mühendisliği bölümü, Isparta, Türkiye , şenol, ramazan isparta uygulamalı bilimler üniversitesi - teknoloji fakültesi - elektrik – elektronik mühendisliği bölümü, Isparta, Türkiye , salman, osamah khaled musleh isparta uygulamalı bilimler üniversitesi - teknoloji fakültesi - mekatronik mühendisliği bölümü, Isparta, Türkiye
Title Of Article
Forecasting oil prices using time series and LSTM model
شماره ركورد
45229
Abstract
In parallel with the increase in the capacity of data storage infrastructures, the variety of data obtained has also increased. By combining these data with time data, it is possible to perform time series analysis on big data. By combining these data with time data, it is possible to perform time-series analysis on big data. Crude oil, which has a very important place in the world economy and is considered black gold; is used in many fields such as industry, transportation, automobile, cosmetics, energy, chemistry and pharmaceutical industries. In the study, a total of 8267 data belonging to the years 1987 and 2020 of Brent Oil prices obtained from the open access website. Since the number of data in the data set used is high and depends on time, the statistical method ARIMA and the deep learning method LSTM models were used in the study. Using the existing data set, the ARIMA and LSTM models have been trained to estimate the 180-day possible prices of oil prices prospectively. It has been observed that the time series analysis of the results and the LSTM model have achieved a significant success in the forward price estimation of Brent Oil.
From Page
34
NaturalLanguageKeyword
Oil Prices , Time Series Analysis , LSTM Model
JournalTitle
Sdu International Technologic Science
To Page
38
JournalTitle
Sdu International Technologic Science
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