Forecasting Currency Exchange Rate Time Series With Fireworks Algorithm-Based Higher Order Neural Network, With Special Attention To Training Data Enrichment

Abstract

Exchange rates are highly fluctuating by nature; thus, they are difficult to forecast. Artificial neural networks (ANNs) have proven to be better than statistical methods. Inadequate training data may lead the model to reach sub-optimal solutions, resulting in poor accuracy (as ANN-based forecasts are data-driven). To enhance forecasting accuracy, we suggests a method of enriching training datasets through exploring and incorporating virtual data points (VDPs) by an evolutionary method called the fireworks algorithm-trained functional link artificial neural network (FWA-FLN). The model maintains a correlation between current and past data, especially at the oscillation point on the time series. The exploration of a VDP and forecast of the succeeding term go consecutively by FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other similarly trained models and produces far better prediction accuracy.