Non-linear autoregressive with exogeneous input (narx) bitcoin price prediction model using pso-optimized parameters and moving average technical indicators
TLDR
The results demonstrated the ability of the model to predict Bitcoin prices accurately while passing all model validation tests.Abstract:
This paper presents a Multi-Layer Perceptron (MLP)-based Non-Linear Autoregressive with 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 combinations to be tested, a Particle Swarm Optimization (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 testsread more
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