This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals

Indrabayu, Indrabayu

Sidehabi, Sitti Wetenriajeng

Tandungan, Sofyan

Hasanuddin University Repository

978-1-5090-5548-7/16/$31.00 ©2016 IEEE Statistical and Machine Learning Approach in Forex Prediction Based on Empirical Data Sitti Wetenriajeng Sidehabi Department of Electrical Engineering Politeknik ATI Makassar Makassar, Indonesia tenri616@gmail.com Indrabayu1, Sofyan Tandungan2 Department of Informatics Engineering Universitas Hasanuddin Makassar, Indonesia indrabayu@unhas.ac.id, standungan@gmail.com  Abstract—This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.  Keywords—forex, prediction, ASTAR. GA-NN, SVM, RMSE I.  INTRODUCTION Forex (Foreign Exchange) is a type of transaction where a party obtains some units in one currency to buy proportion amount in another currency. This exchange is usually conducted in pair currency. The most popular pair and trade worldwide is Euro vs. US Dollar (EUR / USD). In Forex, there are two kinds of  analysis, fundamental and technical analysis. Fundamental term refer to the movement of the market in association  with news or factors that can affect a country's economy, while technical assessment is mainly observed the supply demand trend through market movements by reading charts and indicators of ongoing market price.  In most cases, Forex rates technical prediction are based on statistical charts and machine learning. It is always interesting to measure up both of this procedures in data series prediction, which none of both scheme is likely better than other for each case [1]. A statistical modelling and forecasting using Auto-Regressive Integrated Moving Average (ARIMA) for Gold Bullion Coin has shown promising result with a MAPE (mean absolute percentage error) within 10% [2]. Artificial Intelligence has been researched as well as statistical and machine learning. With a novel approach for efficient weekly market price forecasting, has come to an outstanding result with 99.62% of accurate rate[3]. Recently, A hybrid methods of Artificial Intelligence also fulfill the 30 minutes time frame prediction [4]. This breakthrough allows a practical application for traders in gaining profit within the time frame with all the price indicators i.e. open, close, high and low are predicted as well.  These previous research in price forecasting are conducted thoroughly on single method. This study aim to apply Adaptive Spline Threshold Autoregression (ASTAR), combination of Genetic Algorithm-Neural Network (GA-NN) and Support Vector Machine (SVM) to Forex rates prediction and provide a computational comparison of the performance of these techniques. A. Adaptive Spline Threshold Autoregression (ASTAR) ASTAR is a model obtained from modeling nonlinear time series threshold in Multivariate Adaptive Regression Spline (MARS) method where the predictor is the lagged value of time series data [5]. ASTAR has the ability to generate continuous models with underlying limit cycles when the time series data indicate periodic behaviour. Similar to MARS, ASTAR structured by two complementary algorithm. ASTAR has two stepwise algorithm, which help to get basis functions for model and to get the best appropriate model. First step is forward stepwise algorithm, the model obtained has a very complex structure. Second step is backward stepwise algorithm, basis function in the model from the previous step is turn to reach optimum model. ASTAR model example is as follows:  ܼݐ = ܿ +	∅ଵሺܼ௧ିௗଵ − ݐଵሻା +	∅ଶሺܼ௧ିௗଶ − ݐଶሻା 	+∅ଷሺܼ௧ିௗଵ − ݐଵሻሺܼ௧ିௗଶ − ݐଶሻା + ⋯+	ߝ௧   (1)  where: c  = constants ∅ = coefficient t1, t2  = threshold of each variable Zt-d1, and Zt-d2, d1, d2  = lagged predictor variable.  B. Support Vector Machine (SVM) Support Vector Machine (SVM) is known as a machine learning that uses a pair of input and output data in the form of the desired target. The concept of SVM can be explained simply as the search for the best hyper plane which serves as a separator of two classes in the input space [6].  SVM was developed by Boser, Guyon, Vapnik, and was first presented in 1992 at the Annual Workshop on Computational Learning Theory. The basic concept of SVM is actually a harmonious combination of computational theories that have existed decades earlier, such as margin hyperplane,  kernel and concepts supporting others. However, until 1992, there was no attempt to weave these components.  In contrast to the neural network strategy that seeks hyperplane separation between classes, SVM trying to find the best hyperplane in the input space. The basic principle of SVM is a linear classifier, and further developed in order to work on a non-linear problem by incorporating the concept of the kernel trick on high-dimensional workspace.  The example of linearly separated data is shown in Fig.1. The best hyper plane between two classes can be found by measuring the hyper plane margin and find out the maximum points. Margin is defined as the distance between hyper plane and the closest pattern of each class, which is called support vector. The best hyper plane is defined as the following equation.   ݂ሺݔሻ = ݓܶݔ + ܾ   2)  where x refers to a training pattern, w is referred to as the weight vector and b as the bias term.   Fig. 1. The example of linearly separated data  The types of SVM kernel that is often used to establish the rules of decision, namely:  1. A polynomial machine  ܭሺݔ, ݔ௧ሻ = 	 ሺݔ, ݔ௧ + 1ሻௗ    (3)  where d  is the degree of polynomial kernel  2. A radial basis function machine ܭሺݔ, ݔ௜ሻ = exp−	ቀฮ௫ି	௫ᇲฮଶ௦௜௚మ ቁ   (4)	 3. A two-layer neural network machine  ܭሺݔ, ݔ௧ሻ = 	ߜሾሺݔ, ݔ௧ሻሿ = 	 ଵሾଵାሼ௘௫௣ሺ௩ሺ௫,௫೟ሻି௖ሻሽሿ     (5)  where  and are the parameters of sigmoid function. C. Genetic Algorithm-Neural Network (GANN) Genetic Algorithm (GA) are algorithms that seeks to apply a comprehension of the natural evolution in problem solving tasks. The approach taken by this algorithm is to merge a various solutions at random within a population and then evaluate them to obtain the best solution [4].  In Genetic Algorithm, procedure for finding the best solution is operated simultaneously on a few solutions known as population. Individuals in a population are specified as chromosomes. First population is randomly generated, then the next population is the result of the chromosomes evolution through generation. In every generation, chromosomes will be evaluated using fitness function. Fitness value determine chromosomes quality in the population.  Artificial Neural Network (ANN) is computer science area that attempt to solve real world problems by proposing a powerful solution. ANN has the capability to learn and generate its own knowledge based no its environment. ANN could be used to model complex relation between inputs and outputs to find patterns in data.  Artificial Neural Network (ANN) is a computation system that its architectural and operation based on the knowledge about biological neuron in human brain. ANN is an artificial representation based on human brain that try to copy the learning process of human brain. ANN models has the ability to analyse, predict and associate. ANN ability can be used to learn and generate rules or operation from a few example or given inputs and make a prediction about possible output or save the characteristic of given inputs. II. RESEARCH METHOD A. Input Historical data from 2007 to September 2012 is prepared for training data. Hourly data of Open, High, Low, and Close is obtained from Meta Trader software then divided according to the input data for the designed application. This data will be used to calculate the prediction diagram used as a reference in determining the value of the actual prediction.   Fig. 2. The proposed scheme of forex prediction   B. System design The proposed scheme used for ASTAR, SVM and GANN system shown in Fig. 2.  1) Training : Training data consist of four records such as Open, High, Low, and Close. In ASTAR, each records are trained in order to get best ASTAR model.  For SVM case, training input data treated with Kernel calculation of Radial Basis Function (RBF). In GANN, training data input is the records of Open, High, Low, and Close price. 2) Prediction : The next stage is to find the predicted value based on ASTAR model, SVM, and GANN. Forex historical data in October 2012 is used in this process. Prediction process is conducted to predict forex value for one day, one week and one month. The output from this process will be validated with actual data. 3) Validation : To validate the result from prediction stage, Predicted value will be compared with actual data in October 2012 from Meta Trader. Validation is important part to evaluate the performance of the prediction. Root Mean Square Error (RMSE) is used to determine accuracy of the prediction performance. The smaller the value of RMSE correspond to better accuracy. RMSE is defined by:   ܴܯܵܧ	 = 	ටଵே ∑ ሺݕ݅ −	ݕො݅ሻଶே௜ୀ௛   (6)  where ܰ is the number of data, ݕ݅ represent actual value, and ݕො݅ is the predicted value.  C. Output The Output is the best RMSE from ASTAR model, SVM, and GANN. From a series of simulation, it is found that the best RMSE value is from validation process of one day, one week, and one month time frame.  III. RESULTS AND ANALYSIS A. Adaptive Spline Threshold Autoregression (ASTAR) System ASTAR is a technique to find the best model from a group of data, thus fully utilizing the data ASTAR past and present to make accurate short-term prediction. Prediction in ASTAR system divided into three periods that is prediction in October 1st 2012, October 1st-5th 2012, and all data in October 2012. Figure 3. shown a comparison between actual and prediction result in charts in October 1st 2012. Table 1 shown RMSE value with ASTAR system in October 1st 2012.      Fig. 3. Comparison of Actual and Predicted value in October 1st 2012 for hourly data in ASTAR system  TABLE I. RMSE value for prediction in October 1st 2012 in ASTAR system RMSE Open High Low Close ASTAR 0.001433 0.001209 0.001104 0.001318  1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable OpenActualASTAR1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401.29601 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable HighActualASTAR1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable LowActualASTAR1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24ValueHourPrediction for variable CloseActualASTAR  TABLE II. RMSE value using ASTAR system  ASTAR RMSE Open High Low Close Oct 1st 2012 0.001433 0.001209 0.001104 0.001318 Oct 1st-5st 2012 0.001113 0.000961 0.000978 0.001084 Oct 2012 0.001104 0.001531 0.001043 0.001099  TABLE III. RMSE value using ASTAR system  ASTAR RMSE Open High Low Close October 1st 2012 0.001433 0.001209 0.001104 0.001318 October 1st-5st 2012 0.001113 0.000961 0.000978 0.001084 October 2012 0.001104 0.001531 0.001043 0.001099   Table I shown that the lowest RMSE value is variable Low. Table 2 shows RMSE value for prediction in October 1st 2012, October 1st – 5th 2012, and October 2012.  Table II shows the difference of RMSE values for each variable in different prediction time period. Prediction interval from October 1st to 5th 2012 shown a better performance of ASTAR system than forecasting value in one month period of October 2012.  Table III shows the difference of RMSE values for each variable in different prediction time period. Prediction interval from October 1st to 5th 2012 shown a better performance of ASTAR system than forecasting value in one month period of October 2012.  B. Support Vector Machine (SVM) System The prognosis computing in SVM  system comprise of three periods that is prediction in October 1st 2012, October 1st-5th 2012, and whole data in October 2012. Figure 4 shown a comparison between actual and prediction result in charts in October 1st 2012. Table 3 shown RMSE value with SVM system in October 1st 2012.        Fig. 4. Comparison of Actual and Predicted value in  October 1st 2012 for hourly data in SVM system   1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401.29601 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable OpenActualSVM1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401.29601 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable HighActualSVM1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable LowActualSVM1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401.29601 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24ValueHourPrediction for variable CloseActualSVM TABLE IV. RMSE value for prediction in October 1st 2012 in SVM    RMSE Open High Low Close SVM 0.001827 0.001562 0.001410 0.001781  TABLE V. RMSE value for all prediction in ASTAR system   SVM RMSE Open High Low Close October 1st 2012 0.001827 0.001562 0.001410 0.001781 October 1st-5st 2012 0.001382 0.001205 0.001170 0.001369 October 2012 0.001322 0.001431 0.001215 0.001322  TABLE VI. RMSE value for prediction in October 1st 2012 in GA-NN system   RMSE Open High Low Close GA-NN 0.000559 0.001747 0.001046 0.001322     From Table IV, it is shown that the lowest RMSE value acquired from the Low indicator. Table 4 show RMSE value for prediction in October 1st 2012, October 1st – 5th 2012, and October 2012. Table V shows the difference RMSE values for each variable in different prediction time. Prediction time October 1st until 5th 2012 and October 2012 shown better performance of SVM system when dealing with a lot of data.  C. Genetic Algorithm-Neural Network Prediction in GA-NN system is divided into three intervals that is prediction in October 1st 2012, October 1st-5th 2012, and all data in October 2012. Figure 5 shown a comparison between actual and prediction result in charts in October 1st 2012. Table 5 shown RMSE value with GA-NN system in October 1st 2012. Table VI shown that the lowest RMSE value is variable Open. Table 6 show RMSE value for prediction in October 1st 2012, October 1st – 5th 2012, and October 2012.       Figure 5. Comparison of Actual and Predicted value in  October 1st 2012 for hourly data in GA-NN system   TABLE VII. RMSE value for all prediction in GA-NN system  GA-NN RMSE Open High Low Close October 1st 2012 0.000559 0.001747 0.001046 0.0013218 October 1st-5st 2012 0.000307 0.001345 0.000854 0.0013220 October 2012 0.000293 0.001040 0.001232 0.001295    1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable OpenActualGA-NN1.27001.27501.28001.28501.29001.29501.30001 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable HighActualGA-NN1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401 2 3 4 5 6 7 8 9 101112131415161718192021222324ValueHourPrediction for variable LowActualGA-NN1.27401.27601.27801.28001.28201.28401.28601.28801.29001.29201.29401 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24ValueHourPrediction for variable CloseActualGA-NN TABLE VIII. Comparison of RMSE value for prediction in 1 October 2012   RMSE Open High Low Close ASTAR 0.001433 0.001209 0.001104 0.001318 SVM 0.001827 0.001562 0.001410 0.001781 GA-NN 0.000559 0.001747 0.001046 0.001322  TABLE IX. Comparison of RMSE value for prediction in 1-5 October 2012   RMSE Open High Low Close ASTAR 0.001113 0.000961 0.000978 0.001084 SVM 0.001382 0.001205 0.001170 0.001369 GA-NN 0.000307 0.001345 0.000854 0.001144  TABLE X. Comparison of RMSE value for prediction in October 2012   RMSE Open High Low Close ASTAR 0.001104 0.001531 0.001043 0.001099 SVM 0.001322 0.001431 0.001215 0.001322 GA-NN 0.000293 0.001040 0.001232 0.001295   Table VI shows the difference RMSE values for each variable in different prediction time. Prediction time from October 1st to 5th 2012 shown GA-NN system best performance when dealing with a lot of data.  D. Comparing ASTAR, SVM, and GA-NN System From Table VIII to Table X, For 1 and 5 days intervals, ASTAR shows better results in term of High and Close variable because its data show periodic behaviour. On the contrary GA-NN gives an opposite result for the same variables but shows better results in term of Open and Low. When it comes to longer periods of observations, different results emerge where the Open and High prediction are better with GA-NN and the rest variables is best from ASTAR forecasting. From this point of view, traders would have wider option in the future trading especially in dealing with volatile currency pairs.  IV. CONCLUSION Performance comparison of Statistical and Machine Learning approach has been shown in this paper. From three time periods of observation i.e. 1 day, 5 days, and 30 days, each methods has  benefited outcomes depend on the time periods required. The only exception is SVM that always gives average and normalize results compare to ASTAR and GA-NN.   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Am. Stat. Assoc., vol. 86, no. 416, pp. 864–877, Dec. 1991. [6] C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, Sep. 1995.   

Statistical and Machine Learning Approach in Forex Prediction Based on Empirical Data

Sitti Wetenriajeng Sidehabi

null Indrabayu

Sofyan Tandungan

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Statistical and Machine Learning approach in forex prediction based on empirical data

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Statistical and Machine Learning approach in forex prediction based on empirical data

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