Artificial neural networks applied to forecasting time series.

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

[1]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[2]  James B. McDonald,et al.  Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models , 1999, Comput. Intell..

[3]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[4]  Mitchell B. Chamlin,et al.  A time-series analysis of the impact of heavy drinking on homicide and suicide mortality in Russia, 1956-2002. , 2006, Addiction.

[5]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  Timothy Masters,et al.  Advanced algorithms for neural networks: a C++ sourcebook , 1995 .

[8]  Alfonso Palmer,et al.  Numeric sensitivity analysis applied to feedforward neural networks , 2003, Neural Computing & Applications.

[9]  Albert Sesé,et al.  Designing an artificial neural network for forecasting tourism time series , 2006 .

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

[11]  Philip Hans Franses,et al.  Recognizing changing seasonal patterns using artificial neural networks , 1997 .

[12]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[13]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[14]  Fabian Ramseyer,et al.  Modeling psychotherapy process by time-series panel analysis (TSPA) , 2009, Psychotherapy research : journal of the Society for Psychotherapy Research.

[15]  K. A. Loparo,et al.  Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity , 2008, Clinical Neurophysiology.

[16]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[17]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[18]  J. J. Montaño,et al.  Sensitivity Analysis Applied to Artificial Neural Networks for Forecasting Time Series , 2008 .

[19]  P. Rochon,et al.  Effect of regulatory warnings on antipsychotic prescription rates among elderly patients with dementia: a population-based time-series analysis , 2008, Canadian Medical Association Journal.

[20]  J. Moreno,et al.  Artificial neural networks applied to forecasting time series , 2011 .

[21]  Jae Kyu Lee,et al.  Performance of Neural Networks in Managerial Forecasting , 1993 .

[22]  Aplicación del diseño de series temporales múltiples a un caso de intervención en dos clases de Enseñanza General Básica , 2000 .

[23]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[24]  Hiok Chai Quek,et al.  RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction , 2007, Neurocomputing.

[25]  A. P. Pol,et al.  Redes neuronales artificiales aplicadas al análisis de supervivencia: un estudio comparativo con el modelo de regresión de Co x en su aspecto predictivo , 2002 .

[26]  J. Guydish,et al.  Investigating the Effects of San Francisco's Treatment on Demand Initiative on a Publicly-Funded Substance Abuse Treatment System: A Time Series Analysis , 2009, Journal of psychoactive drugs.

[27]  Alfonso Pitarque,et al.  Las redes neuronales como herramientas estadísticas no paramétricas de clasificación , 2000 .

[28]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[29]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[30]  Stephen F. Witt,et al.  Modeling and Forecasting Demand in Tourism , 1991 .