System identification: theory for the user
System identification: theory for the user
Neural Networks
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A hybrid model for exchange rate prediction
Decision Support Systems
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid SARIMA wavelet transform method for sales forecasting
Decision Support Systems
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Autoregressive integrated moving average (ARIMA) models are one of the most important time series models applied in financial market forecasting over the past three decades. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In the literature, several hybrid techniques have been proposed by combining different time series models together, in order to yield results that are more accurate. In this paper, a new hybrid model of the autoregressive integrated moving average (ARIMA) and probabilistic neural network (PNN), is proposed in order to yield more accurate results than traditional ARIMA models. In proposed model, the estimated values of the ARIMA model are modified based on the distinguished trend of the ARIMA residuals and optimum step length, which are respectively obtained from a probabilistic neural network and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than ARIMA model. Therefore, it can be used as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.