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題名 時間序列預測在匯率及股價混合模型比較分析之研究
A Comparison of Hybrid Models of Time Series Forecasting to Exchange Rate and Stock Index
作者 林漢洲
Lin, Han-Chou
貢獻者 楊亨利
Yang, Heng-Li
林漢洲
Lin, Han-Chou
關鍵詞 隨機漫步
ARIMA
最小平方支持向量迴歸
極限學習機
粒子群
決策樹
Decision Tree
ARIMA
Least squares support vector regression
Empirical mode decomposition
Extreme learning machine
Particle swarm optimization
日期 2018
上傳時間 29-Aug-2018 15:47:36 (UTC+8)
摘要 本研究整合線性方法ARIMA及非線性方法如最小平方支持向量迴歸 (Least Square Support Vector Regression, LSSVR)、極限學習機 (Extreme Learning Machine, ELM)以及資料處理演技術 經驗模態分解(Empirical Mode Decomposition, EMD)，及最佳化方法 粒子群 (Particle Swarm Optimization, PSO)以建立多個混合預測模型，嘗試在匯率及股價的160狀況下 (條件考量5個匯率集+5個指數集共10個資料集x 5個資料長度10%，30%，50%及100%資料比率，及4種訓練資料與全部資料之比率60%，70%，80%及90%) 找到最佳模型。研究結果顯示考量模型精簡性、預測精確度及取代性，對匯率來說，準則為MAPE時，最佳模型為EMD+LSVR and EMD+ELM，準則為DS時，最佳模型為EMD+LSVR 及 LSSVR。對指數來說，準則為MAPE時，最佳模型為EMD+LSSVR and EMD+ELM，準則為DS時，最佳模型為EMD+ELM 及 ELM。
This study builds several hybrid models by combining linear approach ARIMA and nonlinear approaches, such as least squares support vector regression, extreme learning machine, and data processing technique, empirical mode decomposition, and optimizing models by particle swarm optimization to attempt to find the best fitting models among 160 cases generated with (5 exchange rate and 5 Stock Index) datasets * 4 periods * 4 ratios. Results show that considering average MAPE, for exchange rate, the best model is EMD+LSSVR+ELM+PSO, while considering models fitting to data and models simplification, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data and models simplification as well, the best model is EMD+LSVR and LSSVR. For stock index, the best model is EMD+ELM, while considering models fitting to data, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data as well, the best model is EMD+ELM and ELM.
參考文獻 Abu-Mostafa, Y. S. and A. F. Atiya (1996). "Introduction to financial forecasting." Applied Intelligence 6(3): 205-213.
     Assareh, E., M. A. Behrang, M. R. Assari and A. Ghanbarzadeh (2010). "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran." Energy 35(12): 5223-5229.
     Baliyan, A., K. Gaurav and S. K. Mishra (2015). "A Review of Short Term Load Forecasting using Artificial Neural Network Models." Procedia Computer Science 48: 121-125.
     Bao, Y. and Z. Liu (2006). A Fast Grid Search Method in Support Vector Regression Forecasting Time Series. Intelligent Data Engineering and Automated Learning – IDEAL 2006, Berlin, Heidelberg, Springer Berlin Heidelberg.
     Bao, Y., T. Xiong and Z. Hu (2014). "Multi-step-ahead time series prediction using multiple-output support vector regression." Neurocomputing 129: 482-493.
     Bao, Y. K., T. Xiong and Z. Y. Hu (2012). "Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method." Discrete Dynamics in Nature and Society 2012: 12 pages.
     Box, G. E. P. and G. M. Jenkins (1994). Time Series Analysis: Forecasting and Control. NJ, USA, Prentice Hall PTR Upper Saddle River.
     Box, G. E. P., G. M. Jenkins and G. C. Reinsel (1970). Time series analysis, forecasting and control. San Francisco, CA, Holden-Day.
     Braun, M., T. Bernard, O. Piller and F. Sedehizade (2014). "24-Hours Demand Forecasting Based on SARIMA and Support Vector Machines." Procedia Engineering 89: 926-933.
     Cadenas, E. and W. Rivera (2010). "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model." Renewable Energy 35(12): 2732-2738.
     Chaâbane, N. (2014). "A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction." Journal of Applied Statistics 41(3): 635-651.
     Chatfield, C. (1988). "What is the best method of forecasting?" Journal of Applied Statistics 15(1): 19-38.
     Che, J. and J. Wang (2010). "Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling." Energy Conversion and Management 51(10): 1911-1917.
     Chen, C. F., M. C. Lai and C. C. Yeh (2012). "Forecasting tourism demand based on empirical mode decomposition and neural network." Knowledge-Based Systems 26: 281-287.
     Chen, F. L. and T. Y. Ou (2011). "Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry." Expert Systems with Applications 38(3): 1336-1345.
     Chen, K. L., C. C. Yeh and T. L. Lu (2012). "A hybrid demand forecasting model based on empirical mode decomposition and neural network in TFT-LCD industry." Cybernetics and Systems 43(5): 426-441.
     Chen, K. Y. and C. H. Wang (2007). "A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan." Expert Systems with Applications 32(1): 254-264.
     Cheng, C.-H. and L.-Y. Wei (2014). "A novel time-series model based on empirical mode decomposition for forecasting TAIEX." Economic Modelling 36: 136-141.
     de Oliveira, J. F. L. and T. B. Ludermir (2016). "A hybrid evolutionary decomposition system for time series forecasting." Neurocomputing 180: 27-34.
     Deng, S. K., K. Yoshiyama, T. Mitsubuchi and A. Sakurai (2015). "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates." Computational Economics 45(1): 49-89.
     Duan, W. Y., Y. Han, L. M. Huang, B. B. Zhao and M. H. Wang (2016). "A hybrid EMD-SVR model for the short-term prediction of significant wave height." Ocean Engineering 124: 54-73.
     Eberhart, R. C. and J. Kennedy (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya.
     Fishwick, P. A. (1989). Neural Network Models In Simulation: A Comparison With Traditional Modeling Approaches. Simulation Conference Proceedings, 1989. Winter.
     Gang, R. and Z. Zhuping (2011). "Traffic safety forecasting method by particle swarm optimization and support vector machine." Expert Systems with Applications 38(8): 10420-10424.
     GEP, B. and J. GM (1970). Time Series Analysis Forecasting and Control. CA, Holden-Day.
     Granger, C. W. J. (1989). Combining forecasts - twenty years later.
     Gu, J., M. Zhu and L. Jiang (2011). "Housing price forecasting based on genetic algorithm and support vector machine." Expert Systems with Applications 38(4): 3383-3386.
     Guanghui, W. (2012). "Demand Forecasting of Supply Chain Based on Support Vector Regression Method." Procedia Engineering 29: 280-284.
     Guo, X., D. Li and A. Zhang (2012). "Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters." AASRI Procedia 1: 525-530.
     Guo, Z., J. Zhao, W. Zhang and J. Wang (2011). "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China." Energy 36(3): 1668-1679.
     Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten (2009). "The WEKA data mining software: an update." SIGKDD Explor. Newsl. 11(1): 10-18.
     Hassan, M. R., B. Nath and M. Kirley (2007). "A fusion model of HMM, ANN and GA for stock market forecasting." Expert Systems with Applications 33(1): 171-180.
     He, K., K. K. Lai and J. Yen (2010). "A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction." Procedia Computer Science 1(1): 2397-2405.
     Hongwei, Z., Y. Zhenyu and Z. Shurong (2011). "Multi-objective Supervised Clustering GA and Wintry Climate Forecast." Procedia Engineering 15: 1580-1584.
     Huang, C.-J., C.-L. Cheng, Y.-T. Chuang and J.-S. R. Jang (2006). "Admission control schemes for proportional differentiated services enabled internet servers using machine learning techniques." Expert Systems with Applications 31(3): 458-471.
     Huang, G.-B. and C. Siew (2005). Extreme Learning Machine with Randomly Assigned RBF Kernels.
     Huang, G.-B., D. H. Wang and Y. Lan (2011). "Extreme learning machines: a survey." International Journal of Machine Learning and Cybernetics 2(2): 107-122.
     Huang, G.-B., Q.-Y. Zhu and C.-K. Siew (2006). "Extreme learning machine: Theory and applications." Neurocomputing 70(1): 489-501.
     Huang, G. B., L. Chen and C. K. Siew (2006). "Universal approximation using incremental constructive feedforward networks with random hidden nodes." Neural Networks, IEEE Transactions on 17(4): 879-892.
     Huang, G. B., H. Zhou, X. Ding and R. Zhang (2012). "Extreme Learning Machine for Regression and Multiclass Classification." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42(2): 513-529.
     Huang, G. B., Q. Y. Zhu and C. K. Siew (2006). "Extreme learning machine: Theory and applications." Neurocomputing 70(1–3): 489-501.
     Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung and H. H. Liu (1998). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proceedings of the Royal Society A 454(1971): 903-995.
     Huang, N. E., M. L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen and K. L. Fan (2003). "A confidence limit for the empirical mode decomposition and Hilbert spectral analysis." Proceedings of the Royal Society A 459(2037): 2317-2345.
     Huang, S. C., P. J. Chuang, C. F. Wu and H. J. Lai (2010). "Chaos-based support vector regressions for exchange rate forecasting." Expert Systems with Applications 37(12): 8590-8598.
     Ince, H. and T. B. Trafalis (2006). "A hybrid model for exchange rate prediction." Decision Support Systems 42(2): 1054-1062.
     Jaeger, H. and H. Haas (2004). "Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication." Science 304(5667): 78-80.
     Javedani Sadaei, H., R. Enayatifar, F. G. Guimarães, M. Mahmud and Z. A. Alzamil (2016). "Combining ARFIMA models and fuzzy time series for the forecast of long memory time series." Neurocomputing 175: 782-796.
     Jodas, D. S., N. Marranghello, A. S. Pereira and R. C. Guido (2013). "Comparing Support Vector Machines and Artificial Neural Networks in the Recognition of Steering Angle for Driving of Mobile Robots Through Paths in Plantations." Procedia Computer Science 18: 240-249.
     Jones, J. D. (1989). "Univariate ARIMA forecasts of defined variables: the case of real economic variables." Socio-Economic Planning Sciences 23(4): 227-234.
     Kao, L.-J., C.-C. Chiu, C.-J. Lu and C.-H. Chang (2013). "A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting." Decision Support Systems 54(3): 1228-1244.
     Kazem, A., E. Sharifi, F. K. Hussain, M. Saberi and O. K. Hussain (2013). "Support vector regression with chaos-based firefly algorithm for stock market price forecasting." Applied Soft Computing 13(2): 947-958.
     Keerthi, S. S. and C.-J. Lin (2003). "Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel." Neural Computation 15(7): 1667-1689.
     Khandelwal, I., R. Adhikari and G. Verma (2015). "Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition." Procedia Computer Science 48: 173-179.
     Khashei, M. and M. Bijari (2010). "An artificial neural network (p, d, q) model for timeseries forecasting." Expert Systems with Applications 37(1): 479-489.
     Khosravi, A., R. O. Nunes, M. E. H. Assad and L. Machado (2018). "Comparison of artificial intelligence methods in estimation of daily global solar radiation." Journal of Cleaner Production 194: 342-358.
     Krogh, A. and J. Vedelsby (1995). "Neural Network Ensembles, Cross Validation, and Active Learning." Advances in Neural Information Processing Systems 231-238.
     Lahmiri, S. (2016). "Intraday stock price forecasting based on variational mode decomposition." Journal of Computational Science 12: 23-27.
     Landa-Torres, I., E. G. Ortiz-Garcia, S. Salcedo-Sanz, M. J. Segovia-Vargas, S. Gil-Lopez, M. Miranda, J. M. Leiva-Murillo and J. Del Ser (2012). "Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search-Extreme Learning Machine Approach." Ieee Journal of Selected Topics in Signal Processing 6(4): 388-398.
     Lemke, C. and B. Gabrys (2010). "Meta-learning for time series forecasting and forecast combination." Neurocomputing 73(10-12): 2006-2016.
     Li, J. P., L. Tang, X. L. Sun, L. A. Yu, W. He and Y. Y. Yang (2012). "Country risk forecasting for major oil exporting countries: A decomposition hybrid approach." Computers & Industrial Engineering 63(3): 641-651.
     Li, K., C. Hu, G. Liu and W. Xue (2015). "Building`s electricity consumption prediction using optimized artificial neural networks and principal component analysis." Energy and Buildings 108: 106-113.
     Lin, C.-S., S.-H. Chiu and T.-Y. Lin (2012). "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting." Economic Modelling 29(6): 2583-2590.
     Liu, H., H.-q. Tian and Y.-f. Li (2012). "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction." Applied Energy 98: 415-424.
     Liu, H. and J. Wang (2011). "Integrating independent component analysis and principal component analysis with neural network to predict chinese stock market." Mathematical Problems in Engineering 2011: 15.
     Liu, S., L. Xu, D. Li, Q. Li, Y. Jiang, H. Tai and L. Zeng (2013). "Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization." Computers and Electronics in Agriculture 95: 82-91.
     Lohrbach, T. and M. Schumann (1992). Artificial Neural Networks and ARIMA-Models within the Field of Stock Market Prediction - A Comparison A2 - S.C., Society for Worldwide Interbank Financial Telecommunication. Adaptive Intelligent Systems. Amsterdam, Elsevier: 1-18.
     Lu, C.-J., T.-S. Lee and C.-C. Chiu (2009). "Financial time series forecasting using independent component analysis and support vector regression." Decision Support Systems 47(2): 115-125.
     Lu, C. J. (2010). "Integrating independent component analysis-based denoising scheme with neural network for stock price prediction." Expert Systems with Applications 37(10): 7056-7064.
     Lu, C. J., T. S. Lee and C. C. Chiu (2009). "Financial time series forecasting using independent component analysis and support vector regression." Decision Support Systems 47(2): 115-125.
     Lu, C. J. and Y. E. Shao (2012). "Forecasting computer products sales by integrating ensemble empirical mode decomposition and extreme learning machine." Mathematical Problems in Engineering 2012: 15.
     Makridakis, S. (1993). "Accuracy measures: theoretical and practical concerns." International Journal of Forecasting 9(4): 527-529.
     Meese, R. and K. Rogoff (1983). "Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?" Journal of International Economics 14(1-2): 3-24.
     Mohammadi, K., S. Shamshirband, P. L. Yee, D. Petković, M. Zamani and S. Ch (2015). "Predicting the wind power density based upon extreme learning machine." Energy 86: 232-239.
     Mukherjee, S., E. Osuna and F. Girosi (1997). Nonlinear prediction of chaotic time series using support vector machines. IEEE Workshop on Neural Networks for Signal Processing VII.
     Ni, H. and H. Yin (2008). "Self-organising mixture autoregressive model for non-stationary time series modelling." International Journal of Neural Systems 18(6): 469-480.
     Nie, H., G. Liu, X. Liu and Y. Wang (2012). "Hybrid of ARIMA and SVMs for Short-Term Load Forecasting." Energy Procedia 16: 1455-1460.
     Osei-Bryson, K.-M. (2007). "Post-pruning in decision tree induction using multiple performance measures." Computers & Operations Research 34(11): 3331-3345.
     Pai, P. F. and C. S. Lin (2005). "A hybrid ARIMA and support vector machines model in stock price forecasting." Omega 33(6): 497-505.
     Parsopoulos, K. E. and M. N. Vrahatis (2002). Particle swarm optimization method for constrained optimization problems. Proceedings of the 2002 ACM Symposium on Applied Computing.
     Pradeepkumar, D. and V. Ravi (2017). "Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network." Applied Soft Computing 58: 35-52.
     Premanode, B. and C. Toumazou (2013). "Improving prediction of exchange rates using Differential EMD." Expert Systems with Applications 40(1): 377-384.
     Qu, J. and M. J. Zuo (2012). "An LSSVR-based algorithm for online system condition prognostics." Expert Systems with Applications 39(5): 6089-6102.
     Rao, C. R. and S. K. Mitra (1971). Generalized inverse of matrices and its applications. New York, Wiley.
     Saavedra-Moreno, B., S. Salcedo-Sanz, L. Carro-Calvo, J. Gascón-Moreno, S. Jiménez-Fernández and L. Prieto (2013). "Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms." Journal of Wind Engineering and Industrial Aerodynamics 116: 49-60.
     Schwarz, G. (1978). "Estimating the Dimension of a Model." Ann. Statist. 6(2): 461-464.
     Sermpinis, G., K. Theofilatos, A. Karathanasopoulos, E. F. Georgopoulos and C. Dunis (2013). "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization." European Journal of Operational Research 225(3): 528-540.
     Shrivastava, N. A. and B. K. Panigrahi (2014). "A hybrid wavelet-ELM based short term price forecasting for electricity markets." International Journal of Electrical Power & Energy Systems 55: 41-50.
     Sokolov-Mladenović, S., M. Milovančević, I. Mladenović and M. Alizamir (2016). "Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters." Computers in Human Behavior 65: 43-45.
     Sun, Y. (2005). Exchange rate forecasting with an artificial neural network model: can we beat a random walk model? Master of Commerce and Management (MCM), Lincoln University.
     Suykens, J. A. K. and J. Vandewalle (1999). "Least Squares Support Vector Machine Classifiers." Neural Processing Letters 9(3): 293-300.
     Tang, L., L. A. Yu, S. Wang, J. P. Li and S. Y. Wang (2012). "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting." Applied Energy 93: 432-443.
     Tay, F. E. H. and L. Cao (2001). "Application of support vector machines in financial time series forecasting." Omega 29(4): 309-317.
     Timmermann, A. and C. W. J. Granger (2004). "Efficient market hypothesis and forecasting." International Journal of Forecasting 20(1): 15-27.
     Tseng, F.-M., G.-H. Tzeng, H.-C. Yu and B. J. C. Yuan (2001). "Fuzzy ARIMA model for forecasting the foreign exchange market." Fuzzy Sets and Systems 118(1): 9-19.
     Tyree, A. and J. Long Forecasting currency exchange rates: neural networks and the random walk model. Proceedings of the third international conference on artificial intelligence applications, Wall Street.
     Vasilakis, G. A., K. A. Theofilatos, E. F. Georgopoulos, A. Karathanasopoulos and S. D. Likothanassis (2013). "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading." Computational Economics 42(4): 415-431.
     Wang, D.-c., C.-x. Wang, Y.-h. Xie and T.-y. Zhu (2010). Air Temperature Prediction Based on EMD and LS-SVM. 2010 Fourth International Conference on Genetic and Evolutionary Computing.
     Wang, J. D. and W. G. Qi (2009). "Prediction of river water turbidity based on EMD-SVM." Acta Electronica Sinica 37(10): 2130-2133.
     Wang, J. J., J. Z. Wang, Z. G. Zhang and S. P. Guo (2012). "Stock index forecasting based on a hybrid model." Omega 40(6): 758-766.
     Wang, S., L. Yu, L. Tang and S. Y. Wang (2011). "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China." Energy 36(11): 6542-6554.
     Wang, X., J. Wen, Y. Zhang and Y. Wang (2014). "Real estate price forecasting based on SVM optimized by PSO." Optik - International Journal for Light and Electron Optics 125(3): 1439-1443.
     Wang, Y., B. Wang and X. Zhang (2012). "A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction." Procedia Computer Science 9: 1263-1272.
     Wei, Y. and M.-C. Chen (2012). "Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks." Transportation Research Part C: Emerging Technologies 21(1): 148-162.
     Wu, J.-L. and P.-C. Chang (2012). "A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting." Mathematical Problems in Engineering 2012: 1-20.
     Xia, M., Y. Zhang, L. Weng and X. Ye (2012). "Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs." Knowledge-Based Systems 36: 253-259.
     Xie, G., S. Wang and K. K. Lai (2014). "Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches." Journal of Air Transport Management 37: 20-26.
     Xuan, Z. Y. and G. X. Yang (2008). "Application of EMD in the atmosphere time series prediction." Acta Automat Sinica 34(1): 97-101.
     Yan, Q., S. Wang and B. Li (2014). "Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction." Discrete Dynamics in Nature and Society 2014: 1-10.
     Yang, H.-L. and H.-C. Lin (2016). "An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting." International Journal on Artificial Intelligence Tools 25(02): 1650005.
     Yang, H.-L. and H.-C. Lin (2017). "Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting." Computational Economics 49(1): 99-116.
     Yang, H. L. and H.-C. Lin (2015). Integrating emd, chaos-based neural network and pso for financial time series forecasting.
     Yang, X., J. Yuan, J. Yuan and H. Mao (2010). "An improved WM method based on PSO for electric load forecasting." Expert Systems with Applications 37(12): 8036-8041.
     Yang, Y. F., Y. K. Bao, Z. Y. Hu and R. Zhang (2010). "Crude oil price prediction based on empirical mode decomposition and support vector machines." Chinese Journal of Management 7(12): 1884-1889.
     Yaslan, Y. and B. Bican (2017). "Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting." Measurement 103: 52-61.
     Ye, L. and P. Liu (2011). "Combined model based on EMD-SVM for short-term wind power prediction." Proceedings of the Chinese Society of Electrical Engineering 31(31): 102-108.
     Yu, L., W. Dai, L. Tang and J. Wu (2015). "A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting." Neural Computing and Applications 27(8): 2193-2215.
     Yu, L., S. Wang and K. K. Lai (2008). "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm." Energy Economics 30(5): 2623-2635.
     Yu, L. A., W. Dai, L. Tang and J. Q. Wu (2016). "A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting." Neural Computing & Applications 27(8): 2193-2215.
     Yu, Q., Y. Miche, E. Severin and A. Lendasse (2014). "Bankruptcy prediction using Extreme Learning Machine and financial expertise." Neurocomputing 128: 296-302.
     Zhang, G., B. Eddy Patuwo and M. Y. Hu (1998). "Forecasting with artificial neural networks:: The state of the art." International Journal of Forecasting 14(1): 35-62.
     Zhang, G. P. (2003). "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50: 159-175.
     Zhang, Y. and L. Wu (2009). "Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network." Expert Systems with Applications 36(5): 8849-8854.
     Zhu, B. and Y. Wei (2013). "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology." Omega 41(3): 517-524.
     Zhu, B. Z. and Y. M. Wei (2013). "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology." Omega 41: 517-524.
描述 博士
國立政治大學
資訊管理學系
98356508
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098356508
資料類型 thesis
dc.contributor.advisor 楊亨利zh_TW
dc.contributor.advisor Yang, Heng-Lien_US
dc.contributor.author (Authors) 林漢洲zh_TW
dc.contributor.author (Authors) Lin, Han-Chouen_US
dc.creator (作者) 林漢洲zh_TW
dc.creator (作者) Lin, Han-Chouen_US
dc.date (日期) 2018en_US
dc.date.accessioned 29-Aug-2018 15:47:36 (UTC+8)-
dc.date.available 29-Aug-2018 15:47:36 (UTC+8)-
dc.date.issued (上傳時間) 29-Aug-2018 15:47:36 (UTC+8)-
dc.identifier (Other Identifiers) G0098356508en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119717-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 98356508zh_TW
dc.description.abstract (摘要) 本研究整合線性方法ARIMA及非線性方法如最小平方支持向量迴歸 (Least Square Support Vector Regression, LSSVR)、極限學習機 (Extreme Learning Machine, ELM)以及資料處理演技術 經驗模態分解(Empirical Mode Decomposition, EMD)，及最佳化方法 粒子群 (Particle Swarm Optimization, PSO)以建立多個混合預測模型，嘗試在匯率及股價的160狀況下 (條件考量5個匯率集+5個指數集共10個資料集x 5個資料長度10%，30%，50%及100%資料比率，及4種訓練資料與全部資料之比率60%，70%，80%及90%) 找到最佳模型。研究結果顯示考量模型精簡性、預測精確度及取代性，對匯率來說，準則為MAPE時，最佳模型為EMD+LSVR and EMD+ELM，準則為DS時，最佳模型為EMD+LSVR 及 LSSVR。對指數來說，準則為MAPE時，最佳模型為EMD+LSSVR and EMD+ELM，準則為DS時，最佳模型為EMD+ELM 及 ELM。zh_TW
dc.description.abstract (摘要) This study builds several hybrid models by combining linear approach ARIMA and nonlinear approaches, such as least squares support vector regression, extreme learning machine, and data processing technique, empirical mode decomposition, and optimizing models by particle swarm optimization to attempt to find the best fitting models among 160 cases generated with (5 exchange rate and 5 Stock Index) datasets * 4 periods * 4 ratios. Results show that considering average MAPE, for exchange rate, the best model is EMD+LSSVR+ELM+PSO, while considering models fitting to data and models simplification, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data and models simplification as well, the best model is EMD+LSVR and LSSVR. For stock index, the best model is EMD+ELM, while considering models fitting to data, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data as well, the best model is EMD+ELM and ELM.en_US
dc.description.tableofcontents 致謝 I
     摘要 II
     Abstract III
     Table of Contents IV
     List of Tables VI
     List of Figures VIII
     List of Appendix IX
     Chapter 1 Introduction 11
     1.1 Background and motivation 11
     Chapter 2 Related Works 19
     2.2. Introduction of Methods 25
     2.2.1. Random walk 25
     2.2.2. ARIMA 26
     2.2.3. Empirical mode decomposition 27
     2.2.4. Least squares support vector regression 28
     2.2.5. Extreme learning machine 30
     2.2.6. Particle swarm optimization 33
     2.2.7. Decision Tree 34
     Chapter 3 Hybrid Model for Time Series Forecasting 37
     3.1. Framework of Hybrid Models 37
     3.2. Process of comparison of hybrid models 42
     Chapter 4 Experimental Results for Exchange Rate 45
     4.1 Data 45
     4.2 For MAPE 46
     4.3 For DS 53
     Chapter 5 Experimental Results for Stock Index 60
     5.1 Data 60
     5.2 For MAPE 61
     5.3 For DS 68
     Chapter 6 Analysis and Discussion for Exchange Rate 77
     6.1. For MAPE 77
     6.1.1 Rules by decision tree 77
     6.1.2 Pair-Wise Comparison and analysis 79
     6.2. For DS 88
     Chapter 7 Analysis and Discussion for Stock Index 92
     7.1. For MAPE 92
     7.1.1 Rules by decision tree 92
     7.1.2 Pair-Wise Comparison and analysis 94
     7.2. For DS 97
     Chapter 8 Conclusion 101
     8.1. Exchange Rate 101
     8.2. Stock Index 102
     Reference 104
     Appendix 113		zh_TW
dc.format.extent 4687291 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098356508en_US
dc.subject (關鍵詞) 隨機漫步zh_TW
dc.subject (關鍵詞) ARIMAzh_TW
dc.subject (關鍵詞) 最小平方支持向量迴歸zh_TW
dc.subject (關鍵詞) 極限學習機zh_TW
dc.subject (關鍵詞) 粒子群zh_TW
dc.subject (關鍵詞) 決策樹zh_TW
dc.subject (關鍵詞) Decision Treeen_US
dc.subject (關鍵詞) ARIMAen_US
dc.subject (關鍵詞) Least squares support vector regressionen_US
dc.subject (關鍵詞) Empirical mode decompositionen_US
dc.subject (關鍵詞) Extreme learning machineen_US
dc.subject (關鍵詞) Particle swarm optimizationen_US
dc.title (題名) 時間序列預測在匯率及股價混合模型比較分析之研究zh_TW
dc.title (題名) A Comparison of Hybrid Models of Time Series Forecasting to Exchange Rate and Stock Indexen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abu-Mostafa, Y. S. and A. F. Atiya (1996). "Introduction to financial forecasting." Applied Intelligence 6(3): 205-213.
     Assareh, E., M. A. Behrang, M. R. Assari and A. Ghanbarzadeh (2010). "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran." Energy 35(12): 5223-5229.
     Baliyan, A., K. Gaurav and S. K. Mishra (2015). "A Review of Short Term Load Forecasting using Artificial Neural Network Models." Procedia Computer Science 48: 121-125.
     Bao, Y. and Z. Liu (2006). A Fast Grid Search Method in Support Vector Regression Forecasting Time Series. Intelligent Data Engineering and Automated Learning – IDEAL 2006, Berlin, Heidelberg, Springer Berlin Heidelberg.
     Bao, Y., T. Xiong and Z. Hu (2014). "Multi-step-ahead time series prediction using multiple-output support vector regression." Neurocomputing 129: 482-493.
     Bao, Y. K., T. Xiong and Z. Y. Hu (2012). "Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method." Discrete Dynamics in Nature and Society 2012: 12 pages.
     Box, G. E. P. and G. M. Jenkins (1994). Time Series Analysis: Forecasting and Control. NJ, USA, Prentice Hall PTR Upper Saddle River.
     Box, G. E. P., G. M. Jenkins and G. C. Reinsel (1970). Time series analysis, forecasting and control. San Francisco, CA, Holden-Day.
     Braun, M., T. Bernard, O. Piller and F. Sedehizade (2014). "24-Hours Demand Forecasting Based on SARIMA and Support Vector Machines." Procedia Engineering 89: 926-933.
     Cadenas, E. and W. Rivera (2010). "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model." Renewable Energy 35(12): 2732-2738.
     Chaâbane, N. (2014). "A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction." Journal of Applied Statistics 41(3): 635-651.
     Chatfield, C. (1988). "What is the best method of forecasting?" Journal of Applied Statistics 15(1): 19-38.
     Che, J. and J. Wang (2010). "Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling." Energy Conversion and Management 51(10): 1911-1917.
     Chen, C. F., M. C. Lai and C. C. Yeh (2012). "Forecasting tourism demand based on empirical mode decomposition and neural network." Knowledge-Based Systems 26: 281-287.
     Chen, F. L. and T. Y. Ou (2011). "Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry." Expert Systems with Applications 38(3): 1336-1345.
     Chen, K. L., C. C. Yeh and T. L. Lu (2012). "A hybrid demand forecasting model based on empirical mode decomposition and neural network in TFT-LCD industry." Cybernetics and Systems 43(5): 426-441.
     Chen, K. Y. and C. H. Wang (2007). "A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan." Expert Systems with Applications 32(1): 254-264.
     Cheng, C.-H. and L.-Y. Wei (2014). "A novel time-series model based on empirical mode decomposition for forecasting TAIEX." Economic Modelling 36: 136-141.
     de Oliveira, J. F. L. and T. B. Ludermir (2016). "A hybrid evolutionary decomposition system for time series forecasting." Neurocomputing 180: 27-34.
     Deng, S. K., K. Yoshiyama, T. Mitsubuchi and A. Sakurai (2015). "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates." Computational Economics 45(1): 49-89.
     Duan, W. Y., Y. Han, L. M. Huang, B. B. Zhao and M. H. Wang (2016). "A hybrid EMD-SVR model for the short-term prediction of significant wave height." Ocean Engineering 124: 54-73.
     Eberhart, R. C. and J. Kennedy (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya.
     Fishwick, P. A. (1989). Neural Network Models In Simulation: A Comparison With Traditional Modeling Approaches. Simulation Conference Proceedings, 1989. Winter.
     Gang, R. and Z. Zhuping (2011). "Traffic safety forecasting method by particle swarm optimization and support vector machine." Expert Systems with Applications 38(8): 10420-10424.
     GEP, B. and J. GM (1970). Time Series Analysis Forecasting and Control. CA, Holden-Day.
     Granger, C. W. J. (1989). Combining forecasts - twenty years later.
     Gu, J., M. Zhu and L. Jiang (2011). "Housing price forecasting based on genetic algorithm and support vector machine." Expert Systems with Applications 38(4): 3383-3386.
     Guanghui, W. (2012). "Demand Forecasting of Supply Chain Based on Support Vector Regression Method." Procedia Engineering 29: 280-284.
     Guo, X., D. Li and A. Zhang (2012). "Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters." AASRI Procedia 1: 525-530.
     Guo, Z., J. Zhao, W. Zhang and J. Wang (2011). "A corrected hybrid approach for wind speed prediction in Hexi Corridor of China." Energy 36(3): 1668-1679.
     Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten (2009). "The WEKA data mining software: an update." SIGKDD Explor. Newsl. 11(1): 10-18.
     Hassan, M. R., B. Nath and M. Kirley (2007). "A fusion model of HMM, ANN and GA for stock market forecasting." Expert Systems with Applications 33(1): 171-180.
     He, K., K. K. Lai and J. Yen (2010). "A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction." Procedia Computer Science 1(1): 2397-2405.
     Hongwei, Z., Y. Zhenyu and Z. Shurong (2011). "Multi-objective Supervised Clustering GA and Wintry Climate Forecast." Procedia Engineering 15: 1580-1584.
     Huang, C.-J., C.-L. Cheng, Y.-T. Chuang and J.-S. R. Jang (2006). "Admission control schemes for proportional differentiated services enabled internet servers using machine learning techniques." Expert Systems with Applications 31(3): 458-471.
     Huang, G.-B. and C. Siew (2005). Extreme Learning Machine with Randomly Assigned RBF Kernels.
     Huang, G.-B., D. H. Wang and Y. Lan (2011). "Extreme learning machines: a survey." International Journal of Machine Learning and Cybernetics 2(2): 107-122.
     Huang, G.-B., Q.-Y. Zhu and C.-K. Siew (2006). "Extreme learning machine: Theory and applications." Neurocomputing 70(1): 489-501.
     Huang, G. B., L. Chen and C. K. Siew (2006). "Universal approximation using incremental constructive feedforward networks with random hidden nodes." Neural Networks, IEEE Transactions on 17(4): 879-892.
     Huang, G. B., H. Zhou, X. Ding and R. Zhang (2012). "Extreme Learning Machine for Regression and Multiclass Classification." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42(2): 513-529.
     Huang, G. B., Q. Y. Zhu and C. K. Siew (2006). "Extreme learning machine: Theory and applications." Neurocomputing 70(1–3): 489-501.
     Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung and H. H. Liu (1998). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis." Proceedings of the Royal Society A 454(1971): 903-995.
     Huang, N. E., M. L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen and K. L. Fan (2003). "A confidence limit for the empirical mode decomposition and Hilbert spectral analysis." Proceedings of the Royal Society A 459(2037): 2317-2345.
     Huang, S. C., P. J. Chuang, C. F. Wu and H. J. Lai (2010). "Chaos-based support vector regressions for exchange rate forecasting." Expert Systems with Applications 37(12): 8590-8598.
     Ince, H. and T. B. Trafalis (2006). "A hybrid model for exchange rate prediction." Decision Support Systems 42(2): 1054-1062.
     Jaeger, H. and H. Haas (2004). "Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication." Science 304(5667): 78-80.
     Javedani Sadaei, H., R. Enayatifar, F. G. Guimarães, M. Mahmud and Z. A. Alzamil (2016). "Combining ARFIMA models and fuzzy time series for the forecast of long memory time series." Neurocomputing 175: 782-796.
     Jodas, D. S., N. Marranghello, A. S. Pereira and R. C. Guido (2013). "Comparing Support Vector Machines and Artificial Neural Networks in the Recognition of Steering Angle for Driving of Mobile Robots Through Paths in Plantations." Procedia Computer Science 18: 240-249.
     Jones, J. D. (1989). "Univariate ARIMA forecasts of defined variables: the case of real economic variables." Socio-Economic Planning Sciences 23(4): 227-234.
     Kao, L.-J., C.-C. Chiu, C.-J. Lu and C.-H. Chang (2013). "A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting." Decision Support Systems 54(3): 1228-1244.
     Kazem, A., E. Sharifi, F. K. Hussain, M. Saberi and O. K. Hussain (2013). "Support vector regression with chaos-based firefly algorithm for stock market price forecasting." Applied Soft Computing 13(2): 947-958.
     Keerthi, S. S. and C.-J. Lin (2003). "Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel." Neural Computation 15(7): 1667-1689.
     Khandelwal, I., R. Adhikari and G. Verma (2015). "Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition." Procedia Computer Science 48: 173-179.
     Khashei, M. and M. Bijari (2010). "An artificial neural network (p, d, q) model for timeseries forecasting." Expert Systems with Applications 37(1): 479-489.
     Khosravi, A., R. O. Nunes, M. E. H. Assad and L. Machado (2018). "Comparison of artificial intelligence methods in estimation of daily global solar radiation." Journal of Cleaner Production 194: 342-358.
     Krogh, A. and J. Vedelsby (1995). "Neural Network Ensembles, Cross Validation, and Active Learning." Advances in Neural Information Processing Systems 231-238.
     Lahmiri, S. (2016). "Intraday stock price forecasting based on variational mode decomposition." Journal of Computational Science 12: 23-27.
     Landa-Torres, I., E. G. Ortiz-Garcia, S. Salcedo-Sanz, M. J. Segovia-Vargas, S. Gil-Lopez, M. Miranda, J. M. Leiva-Murillo and J. Del Ser (2012). "Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search-Extreme Learning Machine Approach." Ieee Journal of Selected Topics in Signal Processing 6(4): 388-398.
     Lemke, C. and B. Gabrys (2010). "Meta-learning for time series forecasting and forecast combination." Neurocomputing 73(10-12): 2006-2016.
     Li, J. P., L. Tang, X. L. Sun, L. A. Yu, W. He and Y. Y. Yang (2012). "Country risk forecasting for major oil exporting countries: A decomposition hybrid approach." Computers & Industrial Engineering 63(3): 641-651.
     Li, K., C. Hu, G. Liu and W. Xue (2015). "Building`s electricity consumption prediction using optimized artificial neural networks and principal component analysis." Energy and Buildings 108: 106-113.
     Lin, C.-S., S.-H. Chiu and T.-Y. Lin (2012). "Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting." Economic Modelling 29(6): 2583-2590.
     Liu, H., H.-q. Tian and Y.-f. Li (2012). "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction." Applied Energy 98: 415-424.
     Liu, H. and J. Wang (2011). "Integrating independent component analysis and principal component analysis with neural network to predict chinese stock market." Mathematical Problems in Engineering 2011: 15.
     Liu, S., L. Xu, D. Li, Q. Li, Y. Jiang, H. Tai and L. Zeng (2013). "Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization." Computers and Electronics in Agriculture 95: 82-91.
     Lohrbach, T. and M. Schumann (1992). Artificial Neural Networks and ARIMA-Models within the Field of Stock Market Prediction - A Comparison A2 - S.C., Society for Worldwide Interbank Financial Telecommunication. Adaptive Intelligent Systems. Amsterdam, Elsevier: 1-18.
     Lu, C.-J., T.-S. Lee and C.-C. Chiu (2009). "Financial time series forecasting using independent component analysis and support vector regression." Decision Support Systems 47(2): 115-125.
     Lu, C. J. (2010). "Integrating independent component analysis-based denoising scheme with neural network for stock price prediction." Expert Systems with Applications 37(10): 7056-7064.
     Lu, C. J., T. S. Lee and C. C. Chiu (2009). "Financial time series forecasting using independent component analysis and support vector regression." Decision Support Systems 47(2): 115-125.
     Lu, C. J. and Y. E. Shao (2012). "Forecasting computer products sales by integrating ensemble empirical mode decomposition and extreme learning machine." Mathematical Problems in Engineering 2012: 15.
     Makridakis, S. (1993). "Accuracy measures: theoretical and practical concerns." International Journal of Forecasting 9(4): 527-529.
     Meese, R. and K. Rogoff (1983). "Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?" Journal of International Economics 14(1-2): 3-24.
     Mohammadi, K., S. Shamshirband, P. L. Yee, D. Petković, M. Zamani and S. Ch (2015). "Predicting the wind power density based upon extreme learning machine." Energy 86: 232-239.
     Mukherjee, S., E. Osuna and F. Girosi (1997). Nonlinear prediction of chaotic time series using support vector machines. IEEE Workshop on Neural Networks for Signal Processing VII.
     Ni, H. and H. Yin (2008). "Self-organising mixture autoregressive model for non-stationary time series modelling." International Journal of Neural Systems 18(6): 469-480.
     Nie, H., G. Liu, X. Liu and Y. Wang (2012). "Hybrid of ARIMA and SVMs for Short-Term Load Forecasting." Energy Procedia 16: 1455-1460.
     Osei-Bryson, K.-M. (2007). "Post-pruning in decision tree induction using multiple performance measures." Computers & Operations Research 34(11): 3331-3345.
     Pai, P. F. and C. S. Lin (2005). "A hybrid ARIMA and support vector machines model in stock price forecasting." Omega 33(6): 497-505.
     Parsopoulos, K. E. and M. N. Vrahatis (2002). Particle swarm optimization method for constrained optimization problems. Proceedings of the 2002 ACM Symposium on Applied Computing.
     Pradeepkumar, D. and V. Ravi (2017). "Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network." Applied Soft Computing 58: 35-52.
     Premanode, B. and C. Toumazou (2013). "Improving prediction of exchange rates using Differential EMD." Expert Systems with Applications 40(1): 377-384.
     Qu, J. and M. J. Zuo (2012). "An LSSVR-based algorithm for online system condition prognostics." Expert Systems with Applications 39(5): 6089-6102.
     Rao, C. R. and S. K. Mitra (1971). Generalized inverse of matrices and its applications. New York, Wiley.
     Saavedra-Moreno, B., S. Salcedo-Sanz, L. Carro-Calvo, J. Gascón-Moreno, S. Jiménez-Fernández and L. Prieto (2013). "Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms." Journal of Wind Engineering and Industrial Aerodynamics 116: 49-60.
     Schwarz, G. (1978). "Estimating the Dimension of a Model." Ann. Statist. 6(2): 461-464.
     Sermpinis, G., K. Theofilatos, A. Karathanasopoulos, E. F. Georgopoulos and C. Dunis (2013). "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization." European Journal of Operational Research 225(3): 528-540.
     Shrivastava, N. A. and B. K. Panigrahi (2014). "A hybrid wavelet-ELM based short term price forecasting for electricity markets." International Journal of Electrical Power & Energy Systems 55: 41-50.
     Sokolov-Mladenović, S., M. Milovančević, I. Mladenović and M. Alizamir (2016). "Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters." Computers in Human Behavior 65: 43-45.
     Sun, Y. (2005). Exchange rate forecasting with an artificial neural network model: can we beat a random walk model? Master of Commerce and Management (MCM), Lincoln University.
     Suykens, J. A. K. and J. Vandewalle (1999). "Least Squares Support Vector Machine Classifiers." Neural Processing Letters 9(3): 293-300.
     Tang, L., L. A. Yu, S. Wang, J. P. Li and S. Y. Wang (2012). "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting." Applied Energy 93: 432-443.
     Tay, F. E. H. and L. Cao (2001). "Application of support vector machines in financial time series forecasting." Omega 29(4): 309-317.
     Timmermann, A. and C. W. J. Granger (2004). "Efficient market hypothesis and forecasting." International Journal of Forecasting 20(1): 15-27.
     Tseng, F.-M., G.-H. Tzeng, H.-C. Yu and B. J. C. Yuan (2001). "Fuzzy ARIMA model for forecasting the foreign exchange market." Fuzzy Sets and Systems 118(1): 9-19.
     Tyree, A. and J. Long Forecasting currency exchange rates: neural networks and the random walk model. Proceedings of the third international conference on artificial intelligence applications, Wall Street.
     Vasilakis, G. A., K. A. Theofilatos, E. F. Georgopoulos, A. Karathanasopoulos and S. D. Likothanassis (2013). "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading." Computational Economics 42(4): 415-431.
     Wang, D.-c., C.-x. Wang, Y.-h. Xie and T.-y. Zhu (2010). Air Temperature Prediction Based on EMD and LS-SVM. 2010 Fourth International Conference on Genetic and Evolutionary Computing.
     Wang, J. D. and W. G. Qi (2009). "Prediction of river water turbidity based on EMD-SVM." Acta Electronica Sinica 37(10): 2130-2133.
     Wang, J. J., J. Z. Wang, Z. G. Zhang and S. P. Guo (2012). "Stock index forecasting based on a hybrid model." Omega 40(6): 758-766.
     Wang, S., L. Yu, L. Tang and S. Y. Wang (2011). "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China." Energy 36(11): 6542-6554.
     Wang, X., J. Wen, Y. Zhang and Y. Wang (2014). "Real estate price forecasting based on SVM optimized by PSO." Optik - International Journal for Light and Electron Optics 125(3): 1439-1443.
     Wang, Y., B. Wang and X. Zhang (2012). "A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction." Procedia Computer Science 9: 1263-1272.
     Wei, Y. and M.-C. Chen (2012). "Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks." Transportation Research Part C: Emerging Technologies 21(1): 148-162.
     Wu, J.-L. and P.-C. Chang (2012). "A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting." Mathematical Problems in Engineering 2012: 1-20.
     Xia, M., Y. Zhang, L. Weng and X. Ye (2012). "Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs." Knowledge-Based Systems 36: 253-259.
     Xie, G., S. Wang and K. K. Lai (2014). "Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches." Journal of Air Transport Management 37: 20-26.
     Xuan, Z. Y. and G. X. Yang (2008). "Application of EMD in the atmosphere time series prediction." Acta Automat Sinica 34(1): 97-101.
     Yan, Q., S. Wang and B. Li (2014). "Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction." Discrete Dynamics in Nature and Society 2014: 1-10.
     Yang, H.-L. and H.-C. Lin (2016). "An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting." International Journal on Artificial Intelligence Tools 25(02): 1650005.
     Yang, H.-L. and H.-C. Lin (2017). "Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting." Computational Economics 49(1): 99-116.
     Yang, H. L. and H.-C. Lin (2015). Integrating emd, chaos-based neural network and pso for financial time series forecasting.
     Yang, X., J. Yuan, J. Yuan and H. Mao (2010). "An improved WM method based on PSO for electric load forecasting." Expert Systems with Applications 37(12): 8036-8041.
     Yang, Y. F., Y. K. Bao, Z. Y. Hu and R. Zhang (2010). "Crude oil price prediction based on empirical mode decomposition and support vector machines." Chinese Journal of Management 7(12): 1884-1889.
     Yaslan, Y. and B. Bican (2017). "Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting." Measurement 103: 52-61.
     Ye, L. and P. Liu (2011). "Combined model based on EMD-SVM for short-term wind power prediction." Proceedings of the Chinese Society of Electrical Engineering 31(31): 102-108.
     Yu, L., W. Dai, L. Tang and J. Wu (2015). "A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting." Neural Computing and Applications 27(8): 2193-2215.
     Yu, L., S. Wang and K. K. Lai (2008). "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm." Energy Economics 30(5): 2623-2635.
     Yu, L. A., W. Dai, L. Tang and J. Q. Wu (2016). "A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting." Neural Computing & Applications 27(8): 2193-2215.
     Yu, Q., Y. Miche, E. Severin and A. Lendasse (2014). "Bankruptcy prediction using Extreme Learning Machine and financial expertise." Neurocomputing 128: 296-302.
     Zhang, G., B. Eddy Patuwo and M. Y. Hu (1998). "Forecasting with artificial neural networks:: The state of the art." International Journal of Forecasting 14(1): 35-62.
     Zhang, G. P. (2003). "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50: 159-175.
     Zhang, Y. and L. Wu (2009). "Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network." Expert Systems with Applications 36(5): 8849-8854.
     Zhu, B. and Y. Wei (2013). "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology." Omega 41(3): 517-524.
     Zhu, B. Z. and Y. M. Wei (2013). "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology." Omega 41: 517-524.		zh_TW
dc.identifier.doi (DOI) 10.6814/DIS.NCCU.MIS.028.2018.A05en_US