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More services and features. Next, we deal with the forecasting matrix using the PCA technique. Thus, re-combining the new information can further improve the prediction performance of combined forecasts. For further explanation, a simple example is presented.
Original forecasting matrix: 0. Accordingly, its eigenvalues and eigenvectors are computed as 0. Therefore, the reasonable number of individual forecasting methods is determined. This also shows that the former two methods contain enough information to combine forecasts. In the case of the second problem, we propose a nonlinear ensemble forecasting model as a remedy. The detailed model is presented as follows. In this study, ANN is employed to realize nonlinear mapping.
The ability of back-propagation neural networks to represent nonlinear models has been tested by previous work. In fact, the ANN training is a process of searching for optimal weights. That is, this training process made the sum of the square errors minimal, i. To summarize, the proposed nonlinear ensemble model consists of four stages.
Generally speaking, in the first stage we construct an original forecasting matrix according to the selected individual forecasts. In the second stage, the PCA technique is used to deal with the original forecasting matrix and a new forecasting matrix is obtained. In the third stage, based upon the judgments of PCA results, the number of individual forecasting models is determined. And in the final stage, an ANN model is developed to ensemble different individual forecasts, meantime the corresponding forecasting results are obtained.
After completing the proposed nonlinear ensemble model, we want to know whether the proposed model indeed improves forecasting accuracy or not. The basic flow diagram is shown in Fig. A flow diagram of the combined and nonlinear ensemble forecast. Forecasting evaluation criteria As Weigend  argued, a measure normally used to evaluate and compare the predictive power of the model is the normalized mean squared error NMSE this was used to evaluate entries in the Santa Fe Time Series Competition.
Clearly, accuracy is one of the most important criteria for forecasting models—the others being the cost savings and profit earnings generated from improved decisions. From the business point of view, the latter is more important than the former.
For business practitioners, the aim of forecasting is to support or improve decisions so as to make more money. Thus profits or returns are more important than conventional fit measurements. But in exchange rate forecasting, improved decisions often depend L. The ability to forecast movement direction or turning points can be measured by a statistic developed by Yao and Tan .
However, the real aim of forecasting is to obtain profits based on prediction results. Here the annual return rate is introduced as another evaluation criterion. Without considering the friction costs, the annual return rate is calculated according to the compound interest principle.
That is, we use the difference between the predicted value and the actual value to guide trading. We want to use the latter two statistics as the main evaluation criteria because the normalized MSE measure predictions only in terms of levels. Hence, it is more reasonable to choose Dstat and annual return rate R as the measurements of forecast evaluation. Of course, NMSE is also taken into consideration in terms of a comparison of levels. Empirical analysis 3. We take monthly data from January to December as in-sample training periods data sets observations including 24 samples for validation.
We also take the data from January to December as out-of-sample testing periods data sets 36 observations which is used to evaluate the good or bad performance of prediction based on some evaluation measurement. Unfortunately, L. A graphical comparison of DEM rate prediction results using different models — In addition, all the evaluations in this paper with the exception of specifications are based on the whole testing sets.
In order to save space, the original data are not listed here, and detailed data can be obtained from the website or from the authors. The individual ANN model, the linear combination model with minimum-error method and nonlinear ensemble model are built using the Matlab software package, which is produced by Mathworks Laboratory Corporation. The ANN models use trial and error to determine the network architecture of by minimizing the forecasting error with the exception of specifications.
According to the idea shown in Fig. For space reasons the computational processes are omitted but can be obtained from the authors if required. Tables 1—3 show the forecasting performance of different models from different perspectives. From the graphs and tables, we can generally see that the forecasting results are very promising for all currencies under study either where the measurement of forecasting performance is goodness of fit such as NMSE refer to Table 1 or where the forecasting performance criterion is Dstat refer to Table 2.
In detail, Fig. Simi- larly, it can be seen from Fig. The results indicate that the nonlinear ensemble forecasting model performs better than the other models presented here. A graphical comparison of GBP rate prediction results using different models — A graphical comparison of JPY rate prediction results using different models — Subsequently, the forecasting performance comparisons of various models for the three main currencies via NMSE, Dstat and return rate R are reported in Tables 1—3, respectively.
Focusing on the NMSE indicator, the nonlinear ensemble model performs the best in all but the DEM case, followed by the linear combination model with ME method and EW method and other individual models. To summarize, the nonlinear ensemble model and the linear combination model with ME method outperform the other different models presented here in terms of NMSE.
This result differs from that of Zhang . However, the low NMSE does not necessarily mean that there is a high hit rate of forecasting direction for foreign exchange movement direction prediction. Thus, the Dstat comparison is necessary. Focusing on Dstat of Table 2, we find the nonlinear ensemble model also performs much better than the other L.
With reference to Table 2, the differences between the different models are very significant. For the linear combination model with EW method and the hybrid method, the rank of forecasting accuracy is always in the middle for any of the test currencies.
The main cause of this phenomenon is that the bad performance of the individual models has an important effect on the holistic forecast efficiency. Similarly, the individual ANN can model nonlinear time series such as exchange rate series well, and the Dstat rank is also in the middle for any of the test currencies.
The main reason is that the high noise, nonlinearity and complex factors are contained in foreign exchange series, and unfortunately the GLAR is a class of linear model. In terms of the return or profit criterion, the empirical results show that the proposed nonlinear ensemble model could be applied to future forecasting.
Compared with the other models presented in this paper, the nonlinear ensemble forecasting model performs the best. Likewise, we find that the rank of Table 3 is the similar to that of Table 2. It is not hard to understand such a rationale that right forecasting direction often leads to high return rates. As shown in Table 3, for the DEM test case the best return rate for the nonlinear ensemble model is From the experiments presented in this study we can draw the following conclusions.
Like- wise, the nonlinear ensemble model and the linear combination model with ME method also outperform other models in terms of goodness-of-fit or NMSE, as can be seen from Figs. This leads to the third conclusion. Conclusions This study proposes using a nonlinear ensemble forecasting model that combines the time series GLAR model, the ANN model and the hybrid model to predict foreign exchange rates.
In terms of the empirical results, we find that across different forecasting models for the test cases of three main currencies—German marks, British pounds and Japanese yen—on the basis of different criteria, the nonlinear ensemble model performs the best. In the nonlinear ensemble model test cases, the NMSE is the lowest and the Dstat and R is the highest, indicating that the nonlinear ensemble forecasting model can L. References  De Matos G. Neural networks for forecasting exchange rate.
Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics ;— A neural network procedure for selecting predictive indicators in currency trading. In: Refenes AN. Neural networks in the capital markets.
New York: Wiley; Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial Intelligence ;— Forecasting currency prices using a genetically evolved neural network architecture. International Review of Financial Analysis ;— Forecasting exchange rates using general regression neural networks.
Time series forecasting with neural network ensembles: an application for exchange rate prediction. Journal of the Operational Research Society ;— Neurocomputing ;— A hybrid econometric-neural network modeling approach for sales forecasting. International Journal of Production Economics ;— Technological Forecasting and Social Change ;— Combining three estimates of gross domestic product. Economica ;— The combination of forecasts. Operations Research Quarterly ;— The accuracy of extrapolation time series methods: results of a forecasting competition.
Journal of Forecasting ;— Power consumption in West-Bohemia: improved forecasts with decorrelating connectionist networks. Neural Network World ;— Combined neural networks for time series analysis. Neural Information Processing Systems ; — Combining forecasts: a review and annotated bibliography with discussion. International Journal of Forecasting ;— The American heritage dictionary, 4th ed. Boston: Houghton Mifflin; Generalized linear autoregressions.
Economics working paper 8, Nuffield College, Oxford, Basic econometrics, 3rd ed. Time series analysis: forecasting and control. Generalized linear models. Statistical theory and modelling. Time series forecasting using neural networks vs. Box—Jenkins methodology. Simulation ;— Feedforward neural nets as models for time series forecasting. A simulation study of artificial neural networks for nonlinear time-series forecasting.
Insights into neural-network forecasting time series corresponding to ARMA p, q structures. Omega ;— How good are neural networks for causal forecasting?. Journal of Business Forecasting ;— The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression. Regression neural network for error correction in foreign exchange rate forecasting and trading.
Forecaster diversity and the benefits of combining forecasts. Management Science ;— Unstable weights in the combination of forecasts. Improving the accuracy of nonlinear combined forecasting using neural networks. Expert Systems with Application ;— The discussions of many problems about combined forecasting with non-negative weights. Journal of XiDian University ;25 2 —5. Principal component analysis. Berlin: Springer; Generalizations of principal component analysis, optimization problems, and neural networks.
Neural Networks ;8 4 — Time series prediction: forecasting the future and understanding the past. Reading, MA: Addison-Wesley;
Human errors are even more common when faced with analyzing this data. This is why neural networks have the ability to benefit traders greatly. Another major benefit of neural networks is their quick adaptability.
Neural networks do not take a long time to train. This is beneficial as it saves time and resources. Neural networks can help bridge the gap between human intelligence and computers. Neural networks are already in use today. Popular search engines such as Google already use neural networks to improve their system. Google uses neural networks to analyze and classify images, text, and other data.
The neural network has the ability to sort images and distinguish certain features from others. Google translate also utilizes neural networks in part. For example, the translations have become more accurate with the use of neural networks. The benefits of these systems include self-learning, highly improved reaction speed, and problem-solving capabilities.
Neural networks have the ability to make a forecast. They can also generalize and highlight the data as well. The network is trained and can make educated predictions based upon the historical information it has saved. Classical indicators are different from neural networks. Neural networks have the ability to view dependencies between data and therefore make adjustments based upon this information. It will take a level of available time and resources to train the network; however, these are minor and worth the outcome.
As with any other system, neural networks have a margin for error. They can produce an inaccurate forecast. Final solutions mainly rely upon input data. Neural networks can decipher patterns and relationships where a human eye can not. The intelligence of the system has the potential to be faulty as a result of emotion. The lack of emotion can be seen as an Achilles heel in a fluctuating Forex market. My opinion on Neural Networks Neural networks are extremely perspective in science.
They have a unique ability to predict market trends and situations more efficiently than a traditional advisor. They can distinguish patterns, trends, and dynamics. They can discover and detect behavioral cycles. Traders that utilize neural networks prefer long-term trades. Scalpers do not utilize neural networks often. Neural networks existed a decade ago. However, their popularity is increasing as a result of big data.
The technologies associated with big data, such as cloud storage, have rapidly increased the use of neural networks and their potential development. In forex trading, Neural networks have big disadvantages because they can overfit very easily.
How to explain this? When we train the data set, we will get a training error. When we test our model on unseen test data, we will get the test error. Overfitting has an excellent small training error but a huge test error. Our expert advisor will have an excellent small error in forex trading when testing data but terrible results in live trading.
In my experience, simple regression models can be very robust and have excellent live trading performance. GetData values : PrevLayer. Output : PrevLayer. Gradient : PrevLayer. Eine Reihe von Klassenmethoden ist bereits Standard. Schauen wir sie uns an. Am Anfang der Methode rufen wir dann die entsprechende Methode der Elternklasse auf, in der die Basisvariablen und Datenpuffer initialisiert werden.
Dann speichern wir die Stichprobe und setzen die Aktivierungsfunktion der Schicht auf None. Achten Sie bitte auf die Aktivierungsfunktion. Technisch gesehen wird die Aktivierungsfunktion durch den Aufruf der Methode SetActivationFunction der Elternklasse, nach der Initialisierung einer Klasseninstanz, festgelegt. Wenn die Normalisierung entsprechend der Netzarchitektur nach der Aktivierungsfunktion verwendet werden soll, sollte die Aktivierungsmethode in der vorherigen Schicht angegeben werden und es wird keine Aktivierungsfunktion in der Normalisierungsschicht vorhanden sein.
Nach erfolgreichem Anlegen aller Puffer verlassen Sie die Methode mit true. Der gesamte Code aller Klassen und Methoden befindet sich in der Anlage. In diesem Schritt wird die Optimierungsmethode die Struktur des Parameterpuffers vorschlagen. Belassen wir die Verschiebung bei Null. Speichern wir nun einfach die neuen Werte in den Datenpuffern und verlassen den Kernel. GetIndex return false; if! BufferRead return false; BatchOptions. Dann erzeugen und initialisieren wir ggf. Beachten Sie, dass von der Videokarte Daten aus zwei Puffern empfangen werden: Informationen aus der Algorithmusausgabe und ein Parameterpuffer, in dem wir den aktualisierten Mittelwert, die Varianz und den normalisierten Wert gespeichert haben.
Diese Daten werden in weiteren Iterationen verwendet. Beginnen wir mit der Gradientenabstiegsfunktion. Wir speichern den resultierenden Wert in den Gradiententensor der vorherigen Schicht. Wenn sie kleiner oder gleich 1 ist, dann wird die Methode verlassen. Tritt ein Fehler auf, verlassen wir die Methode mit dem Ergebnis false. Activation return false; if!