A Hybrid Forecasting Model for Non-Stationary Time Series : An Application to Container Throughput Prediction

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a [email protected] based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator IPSO-FNN for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles' best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

[1]  Zhang Jun.  Faster particle swarm optimization with random inertia weight , 2009 .

[2]  C. Lumsden.  Culture and the Evolutionary Process, Robert Boyd, Peter J. Richerson. University of Chicago Press, Chicago & London (1985), viii, +301. Price $29.95 , 1986 .

[3]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[4]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[5]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[6]  Ching-Wu Chu,et al.  A comparison of univariate methods for forecasting container throughput volumes , 2009, Math. Comput. Model..

[7]  Thomas Kolarik,et al.  Time series forecasting using neural networks , 1994, APL '94.

[8]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Qingshan Liu,et al.  Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions , 2011, IEEE Transactions on Neural Networks.

[10]  Stephen A. Billings,et al.  Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification , 2009, IEEE Transactions on Neural Networks.

[11]  Paul A. Fishwick,et al.  Time series forecasting using neural networks vs. Box- Jenkins methodology , 1991, Simul..

[12]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[13]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[14]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[15]  Hamidreza Modares,et al.  System Identification and Control using Adaptive Particle Swarm Optimization , 2011 .

[16]  Coskun Hamzaçebi,et al.  Improving genetic algorithms' performance by local search for continuous function optimization , 2008, Appl. Math. Comput..

[17]  Shu-Kai S. Fan,et al.  A hybrid simplex search and particle swarm optimization for unconstrained optimization , 2007, Eur. J. Oper. Res..

[18]  M. Senthil Arumugam,et al.  On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..

[19]  William H. K. Lam,et al.  Forecasts and reliability analysis of port cargo throughput in Hong Kong , 2004 .

[20]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[21]  Kin Keung Lai,et al.  CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY ∗ , 2005 .

[22]  Michael Y. Hu,et al.  A simulation study of artificial neural networks for nonlinear time-series forecasting , 2001, Comput. Oper. Res..

[23]  D.H. Werner,et al.  Particle swarm optimization versus genetic algorithms for phased array synthesis , 2004, IEEE Transactions on Antennas and Propagation.

[24]  H. Brian Hwarng,et al.  Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures , 2001 .

[25]  Philip Hans Franses,et al.  The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production , 2001 .

[26]  Kin Keung Lai,et al.  A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates , 2005, Comput. Oper. Res..

[27]  Lifeng Xi,et al.  Evolving artificial neural networks using an improved PSO and DPSO , 2008, Neurocomputing.

[28]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[29]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[30]  Jiang Chuanwen,et al.  A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation , 2005, Math. Comput. Simul..

[31]  Terry E. Shoup,et al.  A poly-hybrid PSO optimization method with intelligent parameter adjustment , 2011, Adv. Eng. Softw..

[32]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[33]  R. J. Kuo,et al.  A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model , 2011 .

[34]  Jun-Nan Chen,et al.  Forecasting container throughputs at ports using genetic programming , 2010, Expert Syst. Appl..