# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Pattern Based Methods
They used first-order Markov chains and a Markov model of the second-order to find out the best window length, the actual number of windows, and the total number of intervals to increase the predictive efficiency of the system. They considered two types of time series data: EUR/USD one-hour currency pair and WIG20 one-day data, which is a Polish stock exchange index. The performance was measured using the Calmar ratio which shows excellent performance from the system. They used EUR/USD with a candle of one hour, for 50,000 candles. The found a final profit of 7079 pips for 50,000 candles, where pips refer to the price change in the fourth decimal place. They also reached the decision that it is not a guaranteed system that can bring future success, and investing for longer periods is often profitable for stock exchange. [PERSON] and [PERSON] [78] proposed a system wherein they introduced a new approach that was based on the dynamic conditional correlation (DCC). They estimated the parameters by using closing, lowest, and highest rates. They applied the range-based estimator of variance (RBE-V) of returns and the range-based estimator of covariance (RBE-CV) of returns into their DCC model. A range of portfolios of assets were provided by them for the model as it was required by the REB-CV and the suggested model. They compared their system with three different type of existing dynamic conditional correlation models. For GBPUSD, EURUSD, and JPYUSD, the variances gotten were 0.429, 0.238, and 0.192 and the covariances they got were 0.247, 0.422, and 0.145 respectively. An algorithmic framework was developed by the authors of paper [79] which is capable of finding, examining, and utilizing new intra-day rate movements in FOREX. They presented a new method for matching patterns and they created more than 16,000 intra-day rate patterns. They used eight different currencies for their experiment. Their predictive accuracy for finding patterns varied between 60% and 85%. Pattern-based methods have seen quite a popularity surge in these recent three years and seven papers were found based on this method. After reviewing these papers, it was found that all the algorithms used in these papers were different from each other. Recurrent reinforcement learning (RL), dynamic model averaging (DMA), dynamic conditional correlation (DCC) algorithm, etc., were explored by the researchers. Pattern-based approaches also proved their versatile adapt- ability through their uses in different models [73; 74; 75; 76; 77; 78; 79]. Some systems based on patterns can change the statistics of the predictors by using the object properties [75] that can be applied to design a variety of time series systems. However, some pattern-based systems are contradictory to each other, as some models perform well and provide good results with a specific algorithm [76], while other systems perform just the opposite [73]. Additionally, some pattern-based models are only capable of predicting the price over a short-time period and do not guarantee future success for longer period of prediction time [77].