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All Journal Journal of Science and Science Education
Atyanta Nika Rukmasari
Universitas Kristen Satya Wacana
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

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Realized Volatility Forecasting for AI-Mining Sensor Data Using the Multi Layer Perceptron Method Obed Christian Dimitrio; Didit Budi Nugroho; Hanna Arini Parhusip; Atyanta Nika Rukmasari
Journal of Science and Science Education Vol 5 No 2 (2021): JoSSE Vol. 5 No. 2 (November 2021)
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/josse.v5i2p36 - 43

Abstract

This study aims to predict the Realized Volatility (RV) value from AI-Mining sensor data for the period 23 May to 6 June 2022 by using the Multi Layer Preceptron (MLP) method. MLP is the simplest method of artificial neural network. Based on the results obtained after doing MLP with the Python language on Google Colab, the predicted RV value for each data shows a movement in value that is almost similar to the original RV value. The Root Mean Squares Error (RMSE) value for each data prediction is relatively small, which indicates that the MLP method provide accurate prediction on the use of the AI-Mining sensor data to forecast RV.
Co-Authors Didit Budi Nugroho Hanna Arini Parhusip Obed Christian Dimitrio