# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Regression Methods  
[PERSON] and [PERSON] [48] proposed a hybrid model for FOREX prediction that uses wavelet models along with support vector regression (SVR). At first, they used a discrete wavelet transform (DWT) method to interpret information from their FOREX dataset. Then the data were used as the input of support vector regression (SVR) for predicting the FOREX prices. They used five currency pairs which include: CHF/JPY, AUD/JPY, GBP/JPY, EUR/JPY, and EUR/CHF. The performances were compared in ME, RMSE, and MAE. They analyzed the performance of their system with ARIMA and ARFIMA  
Figure 6: Papers based on references. Figure 7: Methods based on citations. models. The results showed that their system performs better than ARIMA and ARFIMA models. For AUD-JPY in a 15 min interval, their model obtained 3.1926 \(\times\) 10\({}^{-3}\) in ME, 4.6395 \(\times\) 10\({}^{-2}\) in RMSE, and 3.4644 \(\times\) 10\({}^{-2}\) in MAE. [PERSON], [PERSON], and [PERSON] [49] proposed a model for time series inspection and prediction that is based on compressed vector autoregression. At first, they used random compression technique to decrease a large number of FOREX data into a smaller form. After that, they used the Bayesian model averaging (BMA) technique to establish the weight of each random compressed datum to obtain the intersecting parameters. Their model can provide out of sample forecasting till fourteen days prior to the actual time. They used 30 FOREX currency pairs for implementing their system and mean squared error for performance evaluation. Most of the currency pairs had a high mean squared error because the predictors used for forecasting were four lagged dependent variables along with random compression of other FOREX currency pairs. They reached the conclusion that their system was not appropriate to predict all the foreign exchange currencies. Their proposed model proved to have good results for six currency pairs--EUR/TRY, CAD/CHF, EUR/DKK, CAD/JPY, EUR/MXN, and AUD/JPY--and outperformed the existing benchmark of Bayesian autoregression. The CAD/JPY, EUR/DKK, and EUR/TRY currency pairs were best currencies when forecast one day to five days prior; and EUR/CHF, EUR/RUB, and EUR/TRY were the best pairs when forecast eight days to fourteen days ahead of the actual time. With combining regression techniques along with the cuckoo search algorithm, a hybrid model for speculating USD/EUR variations was developed by the authors of [41]. They proposed a dataset made of JYN, EUR, and BRP variations and USD/EUR currency pair. In order to train their model, they used their dataset with four regression models and also the generated regression weights of these four algorithms as input to the cuckoo search algorithm. Their proposed model presents good results with multiple linear regression. A huge number of forecasting models were proposed by the authors of the paper [50], by applying linear kernel SVR to historical data for EUROUSD, GBPUSD, and USDJPY currency pairs obtained from high-frequency trading. To forecast the movement of the rates in the next time frame, they used the previous successive timeframes as features. Their datasets contained the finest bid and asking price and the volumes, and in the preprocessed section they removed the volume data along with the asking price. Upon building models, they found a simple rule that provided fine results for systems with single features--that is, return reversal. There were only four regression techniques that matched our review methodology and have been applied for FOREX prediction in these past three years. After reviewing these four papers, it is clear that support vector regression was the most used algorithm which was included in three papers. Compressed vector autoregression, the CRT regression tree, and partial least squares regression were also used by researchers. However, there are other algorithms such as lasso regression, logistic regression, and multivariate regression which were not explored and can be used for forecasting in later years.