# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Neural Network  
[PERSON] [62] proposed a model that uses a fused recurrent Legendre polynomial neural network and an improved shuffled frog leaping for FOREX prediction. She used a high order neural network with a single layer that is combined with Legendre polynomials along with feedback paths. She applied this model on three different currencies, USDAUD, USDINR, and USDGBP. She used RMSE, MAPE, and MAE for evaluation purposes on her proposed model. She evaluated the RLPNN model with three different expansion orders, 2, 3, and 4. For USDAUD, she observed that expansion order 3 provides a better result. Expansion order 2 provided better results for USDINR and USDGBP. For the RMSE model she used different input sizes and database sizes; she got a good result with aperture size 5 and delay block size 2. For RMSE her proposed model got 0.0268, 0.0258, and 0.0301. For MAPE, 0.6977, 0.4773, and 0.3331, and for MAE 0.0303, 0.0281, and 0.0323 for USDAUD, USDINR, and USDGBP respectively. However, due to the expansion order and delay block size, the network size becomes pretty large which is a major drawback of this model. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [45] proposed a model that predicts the time series of FOREX using the C-RNN method. C-RNN was created using a convolutional neural network and recurrent neural network. They used a data-driven technique to observe the changing characteristics of FOREX. They used the past 10 years' data till 2018 for nine different currency pairs. Their dataset contained 2000 datapoints. They compared the model with an algorithm that was created using a convolutional neural network and long short-term memory. Using RMSE they found that their proposed C-RNN model provides less errors than LSTM and CNN. [PERSON] [63] proposed a system that is capable of exchange rate prediction using a combination of a computationally efficient functional-link artificial neural network and improved shuffled frog leaping. Improved shuffled frog leaping was used for reducing the error rate of the system. She used three different currencies, USDCAD, USDJPY, and USDCHF for her proposed system. For comparing the systems performance, she used two different algorithms, the particle swarm optimization algorithm and the shuffled frog leaping algorithm. Monthly prediction was done for all three currencies, and RMSE, MAPE, MAE, and MSE were used to analyze the performance. The proposed model, ISFL-CEFLANN, was run 10 times and the performance was recorded for the comparison. Her system provided better results than the compared algorithms. For RMSE the error rate for USD/CAD and USD/JPY currency pairs was between 0.04 and 0.05 and for USD/CHF the range was between 0.03 and 0.04. [PERSON], [PERSON], and [PERSON] [64] proposed a model called the artificial intelligence risk management system (AIRMS) that is based on machine learning. They developed two risk management systems: One with a neural network (AIRMS-ANN) and another with the decision tree technique (AIRMS-DT). They used five FOREX currencies: EURJPY, GBPUSD, GBPJPY, EURUSD, and USDJPY for implementing those models. They used the technical indicator and past time-series data as the input to their proposed system. They divided the output signal into two categories: profitable and not profitable. When they classified the output signal only as profitable, they got an increase of 50% profit over the two-category classified model. As evaluation metrics, they used the F1 score for both of their models. Both AIRMS-ANN and AIRMS-DT performed well on average and outperformed each other in some cases. When comparing the Kelly criterion portfolios, the decision tree again beat the neural network in terms of total return. [PERSON] [65] proposed a model that uses a higher order neural network for FOREX prediction. They used a shuffled frog leaping algorithm with the Pi-sigma neural network for predicting dynamic and nonlinear currency exchange rates. Three currencies: USDJPY, USDCAD, and USDCHF were used for implementing their model.