in this paper semi-markov models are used to forecast the triple dimensions of next earthquake occurrences. each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. semi-markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. in semi-markov models each zone can be considered as a state...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

in this paper we investigate the long memory of tehran securities price index and fit arfima model using 970 daily data since 1382/1/6 until 1386/4/17. furthermore, we compare the forecasting performance of arfima and arima models. the results show that the series is a long memory one and therefore it can become stationary by fractional differencing. we obtaine the fractional differencing param...

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

applying a precise forecasting method is necessary to achieve acceptable results in ipm programs. performances of the wing and delta pheromone traps for forecasting the codling moth phenology were compared with physiological time data based on degree-hours. six pheromone traps (three wing and three delta style) were applied for the monitoring of the codling moth population. traps were placed in...

In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...