Эволюционное формирование нейросетевых технологий прогнозирования финансовых временных рядов

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One of the expressive and pragmatic applications of artificial intelligence and machine learning is the prediction of financial time series in various markets. The FOREX market is the largest (about $4 trillion daily turnover) international currency market. According to the positive market theory there is a deterministic component in the stochastic price fluctuations on the FOREX market. Therefore, using a fairly accurate predictor it is possible to achieve some speculative success. Recently, an increasing number of papers present the advantage of artificial intelligence methods and machine learning algorithms over the standard econometric methods for solving the problem of financial time series prediction. In particular, neural networks successfully cope with the challenges of the financial forecasting. Thus, the most popular econometric technology for the problem of time series forecasting is called ARIMA [1]. However, in [2] it was shown that the multi-layer perceptron trained by different algorithms outperforms the ARIMA (1, 0, 1) model for the problem of FOREX forecasting. The main problem of artificial neural networks which prevents their widespread exploiting is the challenge of choosing their optimal parameters for a particular problem. There are many parameters to be set up by the user, for example, the type of neural network, the learning algorithm, the number of hidden layers and neurons, activation functions, etc. In addition, the most of modern artificial neural networks have a fixed structure with the predefined types of activation functions. What if the neural network with a more flexible structure will be able to solve a problem more accurately? In this paper we propose a method for evolutionary forming of neural networks, which on the one hand does not require any expert knowledge in the fields of information technology and artificial intelligence from the user and on the other hand creates the neural network with the flexible architecture that could potentially increase the prediction quality. The structure of this article is as follows. Section 2 describes the source data and its statistical characteristics. Section 3 provides the description of the proposed method. Section 4 describes others methods for solving the forecasting problem. The experimental setup is described in Section 5. Section 6 presents the forecasting results of the proposed neural network technology, as well as the comparison to the other forecasting models. The conclusions are done at the end of the paper. Data sets. To test the efficiency of the suggested method the historical data of 13 FOREX currency pairs from 1 January 2000 to 20 July 2012 were used. Each value of the time series is the maximum price during the week. Statistical characteristics of data samples are listed in Table 1. Table 1 the problem of network structural optimization a mapping between a binary vector and a set of different neural network structures should be created. The mapping used in this study is depicted in Fig. 1. Statistical characteristics of data sets Mean Standard deviation AUD/USD 0.771580 0.165808 CHF/JPY 86.539585 9.672273 EUR/CHF 1.501780 0.120486 EUR/GBP 0.736243 0.101301 EUR/JPY 129.387638 19.260813 EUR/USD 1.234870 0.196712 GBP/CHF 2.115349 0.368944 GBP/JPY 180.275463 33.396468 GBP/USD 1.692953 0.185273 NZD/USD 0.733674 0.075432 USD/CAD 1.247870 0.207525 USD/CHF 1.276138 0.256394 USD/JPY 107.203875 14.817511 The largest values of mean and deviation correspond to the currency pairs with Japanese Yen (JPY). Evolutionary forming of neural networks with flexible structure. Standard artificial neural networks have a fixed structure and predefined types of activation functions and connections between neurons. However, there is no fixed structure in the physiological analog of neural networks. What if the neural network with an aibi-trary and flexible structure would be a better model describing available data? Since the structure and all the parameters of the neural network directly affect the final result, this could be a good idea to perform both structural and parametric optimization of the neural network according to the preselected quality criterion. The Mean Absolute Error (Eq. 1) was chosen as the criterion for the quality estimation of the FOREX forecasting. MAE = — УІ (1) x., - xi This metric shows the closeness of the predicted currency rate time series (xt) to the historical values (x. ). In terms of neural networks, the predicted value is a function of the structure and parameters of the neural network, respectively: x,= X (S, P). The challenges for the optimization of the function (1) are the presence of many local minima, high dimensionality and undifferentiated structure in general. Therefore, many researchers prefer to use different heuristic algorithms of the direct search to minimize such functions. Genetic algorithms have proved its high performance for the optimization of complex pseudo-boolean functions. In order to apply the genetic algorithm for solving [Image] Fig. 1. Mapping between the boolean vector and the neural network structure For each neuron there is a sequence of n+4 Boolean values, where n is the number of neurons in the network. The true value means the existence of the synaptic connection between the current neuron and the specified neuron, while false means the absence of such connection. The next 4 bits encode the ordinal number of the activation function. Thus, the number of different activation functions is equal to 16. At each iteration of the structural optimization the synoptic weights of the current network are tuned by the particle swarm optimization algorithm which has shown the best performance. Thereafter the weights of the best found structure are tuned again with more resources to gain the finer model. Alternative algorithms for solving the forecasting problem. To estimate the proposed algorithm the comprehensive comparison with other state-of-the-art techniques was carried out. Multi-layer perceptron with standard back propagation. Multi-layer perceptron (MLP) [3] is a widely used type of the artificial neural network with a layer structure. The standard back propagation (SBP) is the first order algorithm which computes partial derivatives of the error function for all the synaptic weights. These weights are iteratively changed as follows: dE Aw. (n) = -n--baAw,- (n -1) iW dwt 'y ’ where n - is the constant of learning speed, a - is the memory constant. The disadvantage of this technology is the localized nature of the optimization process. Multi-layer perceptron with genetic algorithm. The genetic algorithm (GA) [4] is an effective method for the optimization of pseudo-boolean functions which emulate the processes of natural evolution. Due to the binarization and the Gray code [5] the genetic algorithm is able to optimize the real-number functions. There are many examples in literature showing the advantage of the GA comparing to the classical optimization methods for MLP training, for example in [6]. Multi-layer perceptron with evolution strategy. The adaptation of the genetic algorithm for the direct optimization of the real value function without binarization is called evolution strategy (ES) [7]. The chromosome of the evolution strategy is the set of real values. ES can also be used for the task of MLP learning. The algorithm does not set any limitations on the optimized function. Multi-layer perceptron with particle swarm optimization. Particle swarm optimization (PSO) [8] is the heuristic algorithm of direct search emulating the behavior of bird flocks, fish shoals, etc. Each particle (the solution for optimized problem) is characterized by the vector of speed V (t) and the vector of position X (t). The j-th the speed component of the i-th particle is evaluated as follows: V^ = wV/ + c1rand1Ji (pbest■ — X/ ) + E Xi П F 1 j=1 + c2rand 2J (gbestJ — X/), where с1, с2 — the acceleration coefficients, pbesti — the best position of i-th particle, gbest — the best position of all particles, rand1, rand 2 - uniformly distributed random variables from [0, 1], w is the coefficient of insertion. The coordinates of the new position are calculated iteratively: Xi = Xi—! + V. Thus, each particle approaches both global and local optima. Random variables randl, rand 2 are responsible for the search in the small area of the found optimum points. Multi-layer perceptron with numerical computing of partial derivatives. The optimization of functions with undifferentiated parts can be done using numerical values of partial derivatives. The derivatives can be estimated as follows: F (x0 —S) — F (x0 +s) F (x0) =- 2є These estimations can be used by the optimization algorithm of the first order. In this study, the algorithm of gradient descent was used. Non-parametric Parsen-Rosenblatt’s estimation with genetic algorithm. Non-parametric methods play a special role among the algorithms for the data analysis. Non-parametric Parsen-Rosenblatt's estimator (PR) [9] has been successfully applied for the tasks of modeling, identification and control of complex systems. Among the drawbacks of this method the following ones should be noted: the necessity to configure the smoothing parameters and processing of all training data at each iteration. The optimal smoothing parameters can determine which input arguments have the significant effect on the model, and which ones can be eliminated from the model without serious loss of accuracy. For the data ju^,xi j, where s is the number of samples and n is the dimension of the input space, the non-parametric Parsen-Rosenblatt's estimator is calculated as follows: f vj — uj 1 i=1 XS (v) =■ f vj — uj Л ЕП f i=i where v - the input vector, F - the bell function, for example Gaussian curve, cJs — smoothing parameters (width of the bell function). The quality of non-parametric model strongly depends on the values of the smoothing parameters. Moreover, the smaller value of the smoothing parameter indicates the larger importance of the current variable to the whole model. Optimal smoothing parameters correspond to minimum value of the average square error criterion: ujk — uji 1 s Error (cs) = — E Xk —‘ / \ s n f u — u I ^ nF -j i=l J =1 V cs ) J The minimization of this criterion is performed by the genetic algorithm. Experimental setup. The prediction was carried out by a sliding window method with a window length from 1 till 10 previous currency rate data with the unit step. For all neural networks the number of neurons on the hidden layer ranged from 1 till 20. For a particular type of neural network the optimal prediction window size and the number of neurons were determined experimentally. The prediction quality was determined by the MAE metric. There were 200 neural networks (from 1 to 10 neurons at the input layer and from 1 to 20 neurons at the hidden layer) in each experiment. The activation function for the multi-layer perceptron was the hyperbolic tangent f (w) = th (w). Also, there was a neuron bias [3] in MLP. All available data were separated into training and test 3 1 data in proportion — to — respectively. The best results 4 4 of MSE metric on the test data for each problem are presented in Table 2. Algorithm SBP has been restarted 10 times with the number of epochs n = 7317. The speed parameter was ^ = 0.1, the value of the memory parameter was chosen a = 0.25. There were the following setups and resources in GA for the MLP learning: the number of populations was 271, the number of individuals in each population was 271, the maximum step of binarization (with Gray’s code) was 0.1 and each synopsis weight ranged in [—3,3]. The type of selection was the tournament with the size of the tournament equal to 3, the type of the population forming was elite, the type of recombination was uniform, the type of mutation was normal (the probability of each gin mutation is p = -1, where n is the chromosome size). n For the algorithm of the evolution strategy the following options were used: the number of populations was 271, the size of intermediate population was ц = 41, 1 k=1 the number of individuals in each population was X = 271, the size of parents pool was p = 30, the interval for each synaptic weight [—3,3], the type of recombination was intermediate [7], the type of population forming was (ц + Х). The following options and resources were chosen for the MLP learning algorithm with PSO: c1 = c2 = 2, interval for each neural network weight was [—3,3], the number of swarms was 271, the number of particles in each swarm was 271, the speed limitation was 4 and the constant of the moment was w = 0.81. For the MLP learning by gradient descent the following options were used: the coefficient of learning speed was n = 0.1, the number of steps was 7313, є = 10—8. This algorithms was run 10 times on each neural network. The proposed method of neural network design was set up as follows. For structural optimization the GA with the following options was used: the number of populations was 15, the number of individuals in each population was also 15. The type of selection was tournament with the size of tournament equal to 3, the type of the population forming was elite, the type of the recombination was uniform and strong mutation level (the probabil- 3 ity of each gin mutation was p = —, where n - the chron mosome size). For the parametrical optimization of each neural network structure the particle swarm optimization algorithm with the following parameters was used: c1 = c2 = 2, the interval for each neural network weight was [—3,3], the number of swarms was 15, the number of particles in each swarm was 15, the speed limitation was 4 and the constant of moment was w = 0.81. For the best neural network structure the particle swarm optimization algorithm with the following parameters was used: c1 = c2 = 2, the interval for each neural network weight was [—3,3], the number of swarms was 150, the number of particles in each swarm was 150, the speed limitation was 4 and the constant of moment was w = 0.81. For the task of smoothing parameter optimization by the GA the following settings were chosen. The number of populations was 271, the number of individuals in each population was also 271. The type of selection was tournament with the size of tournament equal to 3, the type of the population forming was elite, the type of the recombination was uniform and the mutation level was strong (the probability of each gin mutation was p = -1, where n n the chromosome size). All the described algorithms were implemented from scratch in C++ language by the authors. The proposed algorithm has shown the best performance on the 7 from 13 forecasting problems. On the other 6 time series the algorithm was only slightly outperformed by the MLP with PSO. The advantage of the suggested method is the automatic determination of all the important aspects, such as the number of neurons on the hidden layer, types of activation functions, connections between neurons, etc.

Об авторах

М. Ю. Сидоров

Сибирский государственный аэрокосмический университет имени академика М. Ф. Решетнева

Email: maxim.sidorov@uniulm.de
магистр техники и технологий, аспирант кафедры диалоговых систем университета города Ульма, Германия. Окончил Сибирский государственный аэрокосмический университет имени академика М. Ф. Решетнева в 2012 г. Область научных интересов - математическое моделирование и оптимизация, нейросетевые технологий, прогнозирование финансовых временных рядов, анализ речевых сигналов.

С. Г. Заблотский

университет города Ульма

Email: sergey.zablotskiy@uni-ulm.de
магистр техники и технологий, аспирант кафедры диалоговых систем университета города Ульма (Германия). Окончил Сибирский государственный аэрокосмический университет имени академика М. Ф. Решетнева в 2007 г. Область научных интересов - математическое моделирование и оптимизация, лексическое и лингвистическое моделирование, автоматическое распознавание речи.

Е. С. Семенкин

Сибирский государственный аэрокосмический университет имени академика М. Ф. Решетнева

Email: eugenesemenkin@yandex.ru
доктор технических наук, профессор, профессор кафедры системного анализа и исследования операций Сибирского государственного аэрокосмического университета имени академика М. Ф. Решетнева. Окончил Кемеровский государственный университет в 1982 г. Область научных интересов - моделирование и оптимизация сложных систем, интеллектуальные информационные технологии, эволюционные алгоритмы.

В. Минкер

университет города Ульма

Email: wolfgang.minker@uni-ulm.de
дважды доктор-инженер, профессор и ассоциативный директор института техники связи университета г. Ульма (Германию). Первую докторскую диссертацию по инженерным наукам защитил в 1997 г. в университете г. Карлсруэ. Вторая докторская диссертация по компьютерным наукам была защищена в 1998 в университете Париж-Юг (Франция). Область научных интересов - диалоговые системы, семантический анализ и моделирование диалогов, оценка качества диалоговых систем

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