Description
Title: A HIGHER ORDER NEURAL NETWORK BASED ON THE FIREWORKS ALGORITHM FORECASTS CURRENCY EXCHANGE RATE TIME SERIES WITH A SPECIAL ATTENTION TO TRAINING DATA ENRICHMENT.
Abstract: Due to their inherent volatility, exchange rates are challenging to predict. Statistical techniques have been shown to be inferior to artificial neural networks (ANNs). Insufficient training data may cause the model to arrive at less-than-ideal solutions, producing poor accuracy (as ANN-based forecasts are data-driven). We propose an approach to enrich training datasets by exploring and incorporating virtual data points (VDPs) using an evolutionary method known as the fireworks algorithm-trained functional link artificial neural network in order to improve forecasting accuracy (FWA-FLN). Particularly at the oscillation point on the time series, the model maintains a correlation between the recent and historical data. FWA-FLN conducts a VDP exploration and future term forecast simultaneously. The proposed model is trained and validated using actual exchange rate time series. The effectiveness of the suggested method is comparable to other models that have been trained similarly, and it yields significantly higher prediction accuracy.
Keywords: exchange rate, virtual data point, interpolation, artificial neural network, fireworks algorithm, functional link neural network
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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