Financial Time Series Forecasting: Comparison of Traditional and Spiking Neural Networks

Abstract

One of the most common applications of neural networks itself is data prediction models, for example, future stock market prices, calculated based on historical data. Spiking neural networks are one of the emerging architectures showing great potential in solving complex problems in complicated information environments. However, to the best of our knowledge, the spiking neural networks have not been successfully applied in stock market data prediction. The values of exchange-traded funds (ETF), due to their flexibility and simplicity, can be a good application of such a tool. Therefore, the following article provides the results of a comprehensive experimental comparison of different spiking neural networks in predicting ETF values. The main goal was to check if the spiking neural networks obtain better or worse results of forecasting than traditional neural networks. The secondary goal was a comparison of different spiking neural network architectures between themselves to judge which one is the most applicable to the given problem