A Comparison of the Effectiveness of Artificial Neural Network Models for Time Series Data Prediction
DOI:
https://doi.org/10.37375/sjfssu.v3i2.1516Keywords:
Stock prices, Prediction models, artificial neural network, Time series data.Abstract
Stock market prediction has become an important area of research. Various methods have been used by researchers to forecast stock market price series. In recent developments, artificial intelligence (AI) methods have become one of the most popular techniques for forecasting financial time series. One of the AI methods that is commonly employed to solve various time series problems is artificial neural network (ANN). This article aims to present a comparative study to evaluate the performance of the three artificial neural network models, namely Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Long Short-Term Memory (LSTM) neural network in a problem of one-day ahead movement prediction of the USD exchange rate. The experiment was performed on the stock price of two companies, namely Microsoft (MSFT) and Apple Inc. (AAPL). The performance of these models is compared using accuracy metrics, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R Squared (R2) score values. Based on the results obtained, the LSTM forecasting model outperformed alternative models with a high degree of accuracy and was found to be very efficient in learning time series data.
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