A combination method for interval forecasting of agricultural commodity futures prices

Proposing an interval forecasting method for agricultural commodity futures prices.Extending the "linear and nonlinear" modeling framework for ITS forecasting.VECM and MSVR are integrated (abbreviated as VECM-MSVR).The experimental analysis is based on one-step-ahead and multi-step-ahead forecasts.VECM-MSVR is a promising method for interval forecasting in future markets. Accurate interval forecasting of agricultural commodity futures prices over future horizons is challenging and of great interests to governments and investors, by providing a range of values rather than a point estimate. Following the well-established "linear and nonlinear" modeling framework, this study extends it to forecast interval-valued agricultural commodity futures prices with vector error correction model (VECM) and multi-output support vector regression (MSVR) (abbreviated as VECM-MSVR), which is capable of capturing the linear and nonlinear patterns exhibited in agricultural commodity futures prices. Two agricultural commodity futures prices from Chinese futures market are used to justify the performance of the proposed VECM-MSVR method against selected competitors. The quantitative and comprehensive assessments are performed and the results indicate that the proposed VECM-MSVR method is a promising alternative for forecasting interval-valued agricultural commodity futures prices.

[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.