A combination method for interval forecasting of agricultural commodity futures prices
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Lu Zhang | Zhongyi Hu | Chongguang Li | Yukun Bao | Tao Xiong | Yukun Bao | Lu Zhang | Chongguang Li | T. Xiong | Zhongyi Hu
[1] Fernando Pérez-Cruz,et al. Multi-dimensional Function Approximation and Regression Estimation , 2002, ICANN.
[2] S. Moshiri,et al. Forecasting Nonlinear Crude Oil Futures Prices , 2006 .
[3] Ping-Feng Pai,et al. A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .
[4] Ramon E. Moore. Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.
[5] S. Johansen,et al. MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY , 2009 .
[6] Francisco de A. T. de Carvalho,et al. Forecasting models for interval-valued time series , 2008, Neurocomputing.
[7] Chunmei Liu. Price Forecast for Gold Futures Based on GA-BP Neural Network , 2009, 2009 International Conference on Management and Service Science.
[8] Carlos Maté,et al. Electric power demand forecasting using interval time series: A comparison between VAR and iMLP , 2010 .
[9] Luca Lambertini. Volatility forecasting for crude oil futures , 2007 .
[10] Chih-Ming Hsu. A hybrid procedure with feature selection for resolving stock/futures price forecasting problems , 2011, Neural Computing and Applications.
[11] Tao Wang,et al. Nonlinearity and Intraday Efficiency Tests on Energy Futures Markets , 2009 .
[12] Ioan Cristian Trelea,et al. The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..
[13] Scott H. Irwin,et al. A reappraisal of the forecasting performance of corn and soybean new crop futures , 1999 .
[14] Fernando Pérez-Cruz,et al. SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems , 2004, IEEE Transactions on Signal Processing.
[15] Alan T. K. Wan,et al. An empirical model of daily highs and lows of West Texas Intermediate crude oil prices , 2010 .
[16] M. Martens. Measuring and Forecasting S&P 500 Index-Futures Volatility Using High-Frequency Data , 2002 .
[17] Andre Luis Santiago Maia,et al. Holt’s exponential smoothing and neural network models for forecasting interval-valued time series , 2011 .
[18] Richard Heaney,et al. Does knowledge of the cost of carry model improve commodity futures price forecasting ability? A case study using the London Metal Exchange lead contract , 2002 .
[19] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[20] Paresh Date,et al. Filtering and forecasting commodity futures prices under an HMM framework , 2013 .
[21] Kuan-Yu Chen,et al. Combining linear and nonlinear model in forecasting tourism demand , 2011, Expert Syst. Appl..
[22] Alan T. K. Wan,et al. A High-Low Model of Daily Stock Price Ranges , 2008, SSRN Electronic Journal.
[23] Chunlu Liu,et al. Construction Price Prediction Using Vector Error Correction Models , 2013 .
[24] Amaury Lendasse,et al. Methodology for long-term prediction of time series , 2007, Neurocomputing.
[25] Jianzhou Wang,et al. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling , 2010 .
[26] Bruno S. Sergi,et al. Modeling and Forecasting Volatility in the Global Food Commodity Prices (Modelování a Prognózování Volatility Globálních cen Potravinářských Komodit) , 2012 .
[27] Víctor Leiva,et al. An R Package for a General Class of Inverse Gaussian Distributions , 2008 .
[28] Francisco de A. T. de Carvalho,et al. Constrained linear regression models for symbolic interval-valued variables , 2010, Comput. Stat. Data Anal..
[29] Duc Khuong Nguyen,et al. Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models , 2012 .
[30] Rob J Hyndman,et al. Automatic Time Series Forecasting: The forecast Package for R , 2008 .
[31] Joseph P. Romano,et al. The stationary bootstrap , 1994 .
[32] Albert P.C. Chan,et al. Construction manpower demand forecasting: A comparative study of univariate time series, multiple regression and econometric modelling techniques , 2011 .
[33] Nikolaos Sariannidis,et al. Nonlinearities in the price behaviour of agricultural products: The case of cotton , 2011 .
[34] Guoqiang Peter Zhang,et al. Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.
[35] George S. Skiadopoulos,et al. Can the Dynamics of the Term Structure of Petroleum Futures be Forecasted? Evidence from Major Markets , 2008 .
[36] Guillaume Chevillon,et al. Direct Multi-Step Estimation and Forecasting , 2006 .
[37] Chenyi Hu,et al. An Application of Interval Methods to Stock Market Forecasting , 2007, Reliab. Comput..
[38] Zhongyi Hu,et al. Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework , 2014, ArXiv.
[39] Amaury Lendasse,et al. Long-term prediction of time series by combining direct and MIMO strategies , 2009, 2009 International Joint Conference on Neural Networks.
[40] Mauro Birattari,et al. Swarm Intelligence , 2012, Lecture Notes in Computer Science.
[41] Eirini Konstantinidi,et al. Are VIX futures prices predictable? An empirical investigation , 2011 .
[42] Javier Arroyo Gallardo,et al. Different approaches to forecast interval time series: a comparison in finance , 2011 .
[43] Zhongyi Hu,et al. Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting , 2014, Knowl. Based Syst..
[44] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[45] Ray Tsaih,et al. Forecasting S&P 500 stock index futures with a hybrid AI system , 1998, Decis. Support Syst..
[46] Angel Pardo,et al. Rolling over stock index futures contracts , 2009 .
[47] P. Hansen. A Test for Superior Predictive Ability , 2005 .
[48] Yin-Wong Cheung,et al. An Empirical Model of Daily Highs and Lows , 2006 .
[49] Perry Sadorsky,et al. Modeling and forecasting petroleum futures volatility , 2006 .
[50] R. Eberhart,et al. Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[51] Zhongyi Hu,et al. Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices , 2013, ArXiv.
[52] Alexander J. Smola,et al. Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.
[53] Bo Liu,et al. An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[54] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[55] 王正華,et al. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan , 2007 .
[56] G. Grudnitski,et al. Forecasting S&P and gold futures prices: An application of neural networks , 1993 .
[57] S. Hamid,et al. Using neural networks for forecasting volatility of S&P 500 Index futures prices , 2004 .
[58] Ashish Sharma,et al. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .
[59] Seong-Min Yoon,et al. Modeling and forecasting the volatility of petroleum futures prices , 2013 .
[60] Javier Arroyo,et al. iMLP: Applying Multi-Layer Perceptrons to Interval-Valued Data , 2007, Neural Processing Letters.