# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Optimization Techniques
For the rest of the datasets, QRRF and QRNN performed better than other models. [PERSON], [PERSON], [PERSON], and [PERSON] (2018) proposed a hybrid system that was build using an extreme learning machine's online sequential model and krill herd (KH). The krill herd (KH) was used for optimized feature reduction of the system. They compared their proposed system with a recurrent backpropagation neural network (RBPNN) and extreme learning machine (ELM) algorithm. They considered three factors: (i) without feature reduction (ii) with statistical feature reduction, and (iii) with optimized feature reduction techniques. For optimized feature reduction techniques, they used bacteria foraging optimization (BFO), krill herd, and particle swarm optimization techniques. They used four currency pairs: SGDINR, YENINR, USDEURO, and USDINR for constructing their model. They used 3, 5, 7, 12, and 15 window periods for their system. While evaluating the model's performance, their system showed promising results. For RMSE, MAPE, and Theil'U evaluation, their system performed best with error rates 0.071646, 0.10375, and 0.00014516 respectively. However,in MAE and ARV performance evaluations, their proposed model failed to provide the best results. For MAE evaluation, the extreme learning machine's online sequential model, and for ARV evaluation, the krill herd with extreme learning machine (KH-ELM) provided the best results of 0.055458 and 0.00092997 respectively. For FOREX trading strategy optimization, a genetic algorithm was employed by the authors of the paper [55] to evolve a different set of profitable trading rules inspired from weighted moving average method. They used a time series with 4147 observations within a range of 2001-2015 and they used the close prices of four currency pairs. For generating rules, their genetic algorithm generates returns significantly higher than the exhausted search. A hybrid machine learning framework was proposed by the authors of the paper [56]. They used a multi-populational Java model for predicting the currency exchange rates. They used two FOREX datasets USDEURO and USDINR from the year 2000 to 2016. Their proposed model was matched with the ELM-Jaya and ELM and their model can forecast the currency prices of foreign exchange rates using technical indicators, statistical measures, and combining both of them. [PERSON] et al. [57] represent a sliding window metaheuristic optimization-based prediction system. It is basically a graphically represented interface, created using MATLAB as a standalone application. This system merges metaheuristic intelligence, the novel firefly algorithm, and a least square support vector regressor and was named METAFALSSVR along with a sliding aperture approach. The authors used a benchmark function for verifying optimization effectiveness. For measuring the performance, they studied two cases--daily CANUSD rates and 4 h EURUSD closing prices, for which they got 0.2532% and 0.169% mean absolute percentage errors. The forecast system obtained a rate of accuracy of 89.8% to 99.7%, which was finer forecasting accuracy than prior models when applied to the CANUSD currency pair comparing to EURUSD exchange price. The error rate of SMOF was 20.8-29.9%, which surpassed the baseline sliding window model. A total of seven papers were found using our searching methods that are based on optimization techniques. After analyzing these papers, it was found that extreme learning machines (ELMs), the Jaya optimization technique, and the swarm optimization algorithm were equally the most used algorithms. The krill herd (KH) and sliding-window metaheuristic optimization were rarely used. Algorithms such as ant colony, whale optimization, route optimization, gray wolf optimization were not used and can be explored further in the future. Optimization technique-based systems provide higher ROIs [52] and can be more accurate when combined with powerful techniques--the genetic algorithm, neural networks, regression, etc. [53; 55; 57].