scholarly journals Prediction of exchange rates using averaging intrinsic mode function and multiclass support vector regression

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Bhsana Premanode ◽  
Jumlong Vonprasert ◽  
Christofer Toumazou
Keyword(s):  
Exchange Rates ◽  
Support Vector Regression ◽  
Intrinsic Mode Function ◽  
Support Vector ◽  
Mode Function

scholarly journals Comparative Study of Forecasting Models for Forex Predictions with the Impact of Different Currencies

2022 ◽  
Vol 12 (1) ◽  
pp. 11-22
Author(s):  
Satria Wiro Agung ◽  
◽  
Kelvin Supranata Wangkasa Rianto ◽  
Antoni Wibowo
Keyword(s):  
Machine Learning ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Short Term Memory ◽  
Political Stability ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

- Foreign Exchange (Forex) is the exchange / trading of currencies from different countries with the aim of making profit. Exchange rates on Forex markets are always changing and it is hard to predict. Many factors affect exchange rates of certain currency pairs like inflation rates, interest rates, government debt, term of trade, political stability of certain countries, recession and many more. Uncertainty in Forex prediction can be reduced with the help of technology by using machine learning. There are many machine learning methods that can be used when predicting Forex. The methods used in this paper are Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR). XGBOOST, and ARIMA. The outcome of this paper will be comparison results that show how other major currency pairs have influenced the performance and accuracy of different methods. From the results, it was proven that XGBoost outperformed other models by 0.36% compared to ARIMA model, 4.4% compared to GRU model, 8% compared to LSTM model, 9.74% compared to SVR model. Keywords— Forex Forecasting, Long Short Term Memory, Gated Recurrent Unit, Support Vector Regression, ARIMA, Extreme Gradient Boosting

Forecasting Exchange Rates

2016 ◽  
pp. 1864-1883
Author(s):  
Ahmed Radhwan ◽  
Mahmoud Kamel ◽  
Mohammed Y. Dahab ◽  
AboulElla Hassanien
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Chaos Theory ◽  
Financial Time Series ◽  
Optimization Technique ◽  
Support Vector ◽  
Data Sets ◽  
Financial Time

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

Forecasting Foreign Exchange Rates Using an SVR-Based Neural Network Ensemble

2008 ◽  
pp. 261-277 ◽  
Author(s):  
Lean Yu ◽  
Shouyang Wang ◽  
Kin Keung Lai
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Foreign Exchange ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Neural Network Ensemble ◽  
Second Stage ◽  
Previous Phase ◽  
Foreign Exchange Rates Forecasting

In this study, a triple-stage support vector regression (SVR) based neural network ensemble forecasting model is proposed for foreign exchange rates forecasting. In the first stage, multiple single neural predictors are generated in terms of diversification. In the second stage, an appropriate number of neural predictors are selected as ensemble members from the considerable number of candidate predictors generated by the previous phase. In the final stage, the selected neural predictors are combined into an aggregated output in a nonlinear way based on the support vector regression principle. For further illustration, four typical foreign exchange rate series are used for testing. Empirical results obtained reveal that the proposed nonlinear neural network ensemble model can improve the performance of foreign exchange rates forecasting.

Forecasting Exchange Rates

2015 ◽  
Vol 2 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Ahmed Radhwan ◽  
Mahmoud Kamel ◽  
Mohammed Y. Dahab ◽  
Aboul Ella Hassanien
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Chaos Theory ◽  
Foreign Exchange Market ◽  
Financial Time Series ◽  
Optimization Technique ◽  
Support Vector ◽  
Financial Time

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection
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