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All Journal Jurnal Informatika Jurnal Teknik Elektro Jurnal Teknik Industri Jurnal Titra Record and Library Journal Journal of Documentation and Information Science International Journal of Industrial Optimization (IJIO)
Siana Halim
Fakulty of Industrial Technology,Industrial Engineering Department, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya 60236
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : International Journal of Industrial Optimization (IJIO)

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Currency movement forecasting using time series analysis and long short-term memory Putri, Kristina Sanjaya; Halim, Siana
International Journal of Industrial Optimization Vol 1, No 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.208 KB) | DOI: 10.12928/ijio.v1i2.2490

Abstract

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.
Co-Authors Adrian Michael Wibisono Agnes Eval Aida Fitriani Tobing Amelia Agnes Sunjono, Amelia Agnes Arif Chandra Cicely Elviani David Christian, David David Soebiantoro Demmy Kasih, Demmy Dian Wulandari Felecia - Felecia . Felecia Felecia Felicia Tedja Widjaja, Felicia Tedja Genesius Chandra Hendrik Sumawi Herman Mallian Indriati Bisono Indriati N Bisono Inggrid -, Inggrid Inggrid Inggrid Jani Rahardjo Johan Widjaja Njo Anastasia Olivia Sigit, Olivia Oviliani Yenty Yuliana Pauline Astari Singgih Putri, Kristina Sanjaya Shirley Adelia Stefanie Hartanto Tan Didik Wardoyo Tanusi Tanti Octavia Valencia Suseno veronica - Yohan Wahyudi Yuliana Vina Humira