Articlediamond2018

Non-linear Autoregressive with Exogeneous input (narx) bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators

N.I. Indera · Ahmad Ihsan Mohd Yassin · Azlee Zabidi · Zairi Ismael Rizman

93 citations0/0 files storedJournal of Fundamental and Applied Sciences

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Stock Market Forecasting Methods Blockchain Technology Applications and Security Forecasting Techniques and Applications

Abstract

This paper presents a Multi-Layer Exogeneous Inputs (NARX) Bitcoin price forecasting model using the opening, closing, minimum and maximum past prices together with Moving Average (MA) technical indicators.As there were many parameter (PSO)-based method was used to optimize the number of hidden units, input lag and output lag of the NARX model.The results demonstrated the ability of the model to predict Bitcoin prices accurately while passing all model validation tests.

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References

2014Stock Price Prediction Using the ARIMA Model 2015A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price 2012Stock Market Prediction Using Artificial Neural Networks 2012Stock Price Prediction using Neural Network with Hybridized Market Indicators

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