# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Neural Network
The ISFL algorithm was used for estimating the hidden parameters and improving the prediction rate. ISFL algorithm is an improved version of the shuffled frog leaping algorithm (SFLA) wherein convergence the speed of the network is improved along with the predictive ability of the network. They compared the performance of their model with a variety of different models. Their model provided better accuracy along with better statistical performance. For USDJPY, USDCAD, and USDCHF currency pairs the error rates they got using RMSE were 0.0167, 0.0195, and 0.0210 respectively. Using MAPE the error rates they got were 0.5025, 0.2616, and 0.4476 respectively, and in MAE the error rates were 0.0150, 0.0137, and 0.0171 respectively. [PERSON], [PERSON], [PERSON], and [PERSON] [66] proposed a new hybrid model for foreign exchange currency price volatility forecasting. They created a new neural network-based system. For building this model, they generated a preset number of imitate data sequences using the GARCH model and other input data variables. They tuned the data sequences and other variables by using particle swarm optimization algorithm to find the best features that provide more accurate prediction of volatility. They used the EUR/USD dataset for the implementation of their model. They found that simulated series created using GARCH model improves the neural network's prediction ability. They used three GARCH models: EGARCH, GJR-GARCH, and GARCH to find the best performing model. While evaluating the 10 day and 15 day prediction data, they found that EGARCH provided the best result in terms of MAPE, MAE, RMSE, and MFE performance evaluation. [PERSON] et al. [67] analyzed the correlation between Taiwan Weighted Stock and Google trend. They used the search volume issued by the Google trend for root testing and finding the connection. Then they analyzed their obtained data and did two experiments, e.g., ML and searching trends. After performing the empirical analysis, the result showed that the support vector machine and decision tree did not perform well as compared with the neural network. Then they compared the NN with search trends in the second experiment wherein the return value was higher. The neural network was quite popular in recent years. A total of seven papers were found on neural networks using our searching method. After analyzing those papers, it was found that different types of neural networks were used by the researchers, such as the recurrent Legendre polynomial neural network and the Pi-Sigma neural network. Many algorithms, such as the modular neural network and deep belief model are yet to be explored. The reviewed literature indicate that neural network-based models can be equipped with different types of approaches, which proves the versatility of these models [62; 63; 64; 65; 66; 67]. Moreover, instead of manual audit trails, neural networks have been applied to the financial distress problem, audit fees, internal control risk assessments, etc. [68]. However, in some systems, a large network size may affect the result to a great extent [62].