# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Rest of the Methods  
[PERSON], [PERSON], [PERSON], and [PERSON] [80] proposed a model for FOREX time series prediction by combining a variety of machine learning models predicting results. They used support vector machine (SVM), Bayesian autoregressive trees (BART), random forest (RF), naive Bayes (NB), and dense layer neural network (NN) classifiers for modeling the major currencies' dependency patterns. Depending on the outputs of these algorithms and the information obtained from correlations of their dataset, they created implied signals for exchange price fluctuation. As the last step they turned those signals into predictive waveforms using genetic algorithm optimization, leveraging majority voting, and regression weighting techniques. They used the moving-window training approach wherein they tried to imitate the FOREX market's dynamic changes. They took all the major currency pairs for training and testing their system. They used MAE, MAPE, and RMSE for evaluating the performance of their model. For SVM, RF, BART, NN, and AR, using RMSE they got 0.74%, 0.73%, 0.75%, 0.81%, and 0.84% respectively. Similarly, for MAE, AR performed better than other algorithms. For generating a predictive waveform, the genetic algorithm performs better than other two algorithms. From the results they found that for the USD/CAD currency pair their proposed model performed best and for USD/NZD their system performed the worst. To select a good set of FOREX experts, an improved expert selection model was proposed by the authors of the paper [81], by learning previously observed sequences. In their model they considered the average mistakes made and average earned by experts, to achieve better performance. They used two real major currency-pair datasets with a period of 29-Apr-2013 to 25-Apr-2014 which consisted of 260 predictions. They proposed their model with good prediction accuracy by RWMAprofit and for profit, the RWMAdual method was efficient. The context information-based FOREX service model was developed by the authors of the paper [82]. They proposed a C-IOB (context-information, observation, belief) graphical structure model which shows a causal relationship between low-level and high-level context. Their proposed model provides accurate context-aware services and it has the capability to adapt to real-time situations of business needs by deducing belief based on observations. Their simulation results show that the time to execute FOREX transactions is less with context-based beliefs. [PERSON] and [PERSON] [83] proposed a new hybrid model that combines DRNN and ARIMA models for FOREX prediction. They extracted the input features and used them as the input of DRNN. DRNN predicts the next price value and also feeds the filtered version of data to ARIMA. ARIMA generates the forecast deviation and after combining the results from both DRNN and ARIMA the closing price is obtained. Their model contained three hidden layers, each with 10 nodes, and it was run for 2000 epochs. They used the EUR/USD currency pair data for their proposed model. Their result showed that when the number of epochs or the number of input features increased, the model overfitted and resulted in poor accuracy for the test examples. They concluded that the best result was obtained when they used the current closing price along with the RSI indicator value and EMA indicator values as input features. A NLP-based model was proposed by the authors [43] using news headlines. They predicted the upward and downward trend of a FOREX currency pair. They focused on determining two things: the proper sentiment of news headlines and proper sense of textual contents to improve the sentiment analysis. They introduced a different novel approach called relevant gloss retrieval, threshold-verb normalization for properly distinguishing the sentiments. They used a sample dataset which contains 15 news headlines with 71 different word pairs and got an accuracy 0 83.33%.