Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

Kishore Kumar Sahu, Sarat Chandra Nayak, Himanshu Sekhar Behera

Abstract


Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.

Keywords


exchange rate;virtual data point, interpolation; artificial neural network; fireworks algorithm; functional link neural network

Full Text:

PDF

References


Q. Li, Y. Chen, J. Wang, Y. Chen, and H. Chen, “Web media and stock markets: A survey and future directions from a big data perspective,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 2, pp. 381–399, 2017.

A. K. Nassirtoussi, S. Aghabozorgi, T. Ying Wah, and D. C. L. Ngo, “Text mining for market prediction: A systematic review,” Expert Syst. Appl., vol. 41, no. 16, pp. 7653–7670, 2014.

L. Anastasakis and N. Mort, “Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach,” Expert Syst. Appl., vol. 36, no. 10, pp. 12001–12011, 2009.

Y. Aiba, N. Hatano, H. Takayasu, K. Marumo, and T. Shimizu, “Triangular arbitrage in the foreign exchange market,” in The Application of Econophysics, Springer, 2004, pp. 18–23.

D. J. Fenn, S. D. Howison, M. McDonald, S. Williams, and N. F. Johnson, “The mirage of triangular arbitrage in the spot foreign exchange market,” Int. J. Theor. Appl. Financ., vol. 12, no. 08, pp. 1105–1123, 2009.

S. Drożdż, J. Kwapień, and R. Oświȩcimka Pawełand Rak, “The foreign exchange market: return distributions, multifractality, anomalous multifractality and the Epps effect,” New J. Phys., vol. 12, no. 10, p. 105003, 2010.

C. Engel, N. C. Mark, and K. D. West, “Factor model forecasts of exchange rates,” Econom. Rev., vol. 34, no. 1–2, pp. 32–55, 2015.

A. Ismailov and B. Rossi, “Uncertainty and deviations from uncovered interest rate parity,” J. Int. Money Financ., vol. 88, pp. 242–259, 2018.

P. M. Pincheira and F. Neumann, “Can we beat the Random Walk? The case of survey-based exchange rate forecasts in Chile,” Case Surv. Exch. Rate Forecast. Chile (December 9, 2018), 2018.

T. E. Clark and K. D. West, “Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis,” J. Econom., 2006.

T. E. Clark and K. D. West, “Approximately normal tests for equal predictive accuracy in nested models,” J. Econom., 2007.

P. De Grauwe and A. Markiewicz, “Learning to forecast the exchange rate: Two competing approaches,” J. Int. Money Financ., vol. 32, pp. 42–76, 2013.

N. Meade, “A comparison of the accuracy of short term foreign exchange forecasting methods,” Int. J. Forecast., vol. 18, no. 1, pp. 67–83, 2002.

J. Yao and C. L. Tan, “A case study on using neural networks to perform technical forecasting of forex,” Neurocomputing, vol. 34, no. 1–4, pp. 79–98, 2000.

J. Kamruzzaman, R. A. Sarker, and I. Ahmad, “SVM based models for predicting foreign currency exchange rates,” in Third IEEE International Conference on Data Mining, 2003, pp. 557–560.

R. J. Kuo, C. H. Chen, and Y. C. Hwang, “An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network,” Fuzzy sets Syst., vol. 118, no. 1, pp. 21–45, 2001.

K. K. Sahu, S. Panigrahi, and H. S. Behera, “A novel chemical reaction optimization algorithm for higher order neural network training,” J. Theor. Appl. Inf. Technol., vol. 53, no. 3, pp. 402–409, 2013.

K. K. Sahu, G. R. Biswal, P. K. Sahu, S. R. Sahu, and H. S. Behera, “A CRO based FLANN for forecasting foreign exchange rates using FLANN,” Smart Innov. Syst. Technol., vol. 31, pp. 647–664, 2015.

S. Bhattacharyya, O. V Pictet, and G. Zumbach, “Knowledge-intensive genetic discovery in foreign exchange markets,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 169–181, 2002.

C. Neely, P. Weller, and R. Dittmar, “Is technical analysis in the foreign exchange market profitable? A genetic programming approach,” J. Financ. Quant. Anal., vol. 32, no. 4, pp. 405–426, 1997.

W. Shen, X. Guo, C. Wu, and D. Wu, “Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm,” Knowledge-Based Syst., vol. 24, no. 3, pp. 378–385, 2011.

R. Dash, “Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction,” Appl. Soft Comput., vol. 67, pp. 215–231, 2018.

R. Dash, “An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction,” Phys. A Stat. Mech. its Appl., vol. 486, pp. 782–796, 2017.

R. Dash and P. K. Dash, “Prediction of financial time series data using hybrid evolutionary Legendre neural network: Evolutionary LENN,” Int. J. Appl. Evol. Comput., vol. 7, no. 1, pp. 16–32, 2016.

R. Dash, “DECPNN: A hybrid stock predictor model using Differential Evolution and Chebyshev Polynomial neural network,” Intell. Decis. Technol., vol. 12, no. 1, pp. 93–104, 2018.

R. Dash, P. K. Dash, and R. Bisoi, “A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction,” Swarm Evol. Comput., vol. 19, pp. 25–42, 2014.

R. Dash and P. Dash, “Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique,” Expert Syst. Appl., vol. 52, pp. 75–90, 2016.

K. K. Sahu, S. R. Sahu, G. R. Biswal, P. K. Sahu, and H. S. Behera, “Chemical reaction optimisation : a hybrid technique applied to functional link artificial neural networks with least mean square learning for foreign exchange rates forecasting Kishore Kumar Sahu *, Soumya Ranjan Sahu , Gyana Ranjan Biswal , Prabin Kumar,” Int. J. Swarm Intell., vol. 2, pp. 254–282, 2016.

K. K. Sahu, S. R. Sahu, S. C. Nayak, and H. S. Behera, “Forecasting foreign exchange rates using CRO based different variants of FLANN and performance analysis,” Int. J. Comput. Syst. Eng., vol. 2, no. 4, p. 190, 2016.

Y. Tan and Y. Zhu, “Fireworks algorithm for optimization,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010.

Y. S. Abu-Mostafa, “Financial Applications of Learning from Hints,” in Advances in Neural Information Processing Systems 7, 1995, pp. 411–418.

G. An, “The Effects of Adding Noise during Backpropagation Training on a Generalization Performance,” Neural Comput., vol. 8, no. 3, pp. 643–674, 1996.

T. Jo, “VTG schemes for using back propagation for multivariate time series prediction,” Appl. Soft Comput., vol. 13, no. 5, pp. 2692–2702, 2013.

S. C. Nayak, B. B. Misra, and H. S. Behera, “Efficient forecasting of financial time-series data with virtual adaptive neuro-fuzzy inference system,” Int. J. Bus. Forecast. Mark. Intell., vol. 2, no. 4, pp. 379–402, 2016.

S. C. Nayak, B. B. Misra, and H. S. Behera, “Exploration and incorporation of virtual data positions for efficient forecasting of financial time series,” Int. J. Ind. Syst. Eng., vol. 26, no. 1, pp. 42–62, 2017.

S. C. Nayak, B. B. Misra, and H. S. Behera, “Efficient financial time series prediction with evolutionary virtual data position exploration,” Neural Comput. Appl., vol. 31, no. 2, pp. 1053–1074, 2019.

Y. H. Pao, “The Functional Link Net: Basis for an Integrated Neural-Net Computing Environment,” Adapt. Pattern Recognit. Neural Networks, Addisson-Wesley, Reading, MA, pp. 197–222, 1989.

G. E. P. Box and D. A. Pierce, “Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,” J. Am. Stat. Assoc., 1970.

D. R. Cox and A. Stuart, “Some Quick Sign Tests for Trend in Location and Dispersion,” Biometrika, 1955.

R. Bartels, “The rank version of von Neumann’s ratio test for randomness,” J. Am. Stat. Assoc., 1982.

S. Gibbons, J. D. and Chakraborti, “Nonparametric Statistical Inference,” Biometrics, vol. 67, no. 3, pp. 1182–1183, 2011.

S. C. Nayak, B. B. Misra, and H. S. Behera, “Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices,” Ain Shams Eng. J., vol. 8, no. 3, pp. 371–390, 2017.

S. C. Nayak, B. B. Misra, and H. S. Behera, “ACFLN: artificial chemical functional link network for prediction of stock market index,” Evol. Syst., 2018.




DOI: https://doi.org/10.7494/csci.2020.21.4.3474

Refbacks

  • There are currently no refbacks.