Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by...Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.展开更多
This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare...This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.展开更多
This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-<...This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">generalized conditional heteroskedastisticty (SARFIMA-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> derived. Test statistics that are useful to examine if seasonal level shift in a</span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;"> SARFIMA-GARCH model </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> also considered.</span>展开更多
文摘Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.
文摘This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.
文摘This paper introduces the class of seasonal fractionally integrated autoregressive<span style="font-family:Verdana;"> moving average</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">generalized conditional heteroskedastisticty (SARFIMA-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> derived. Test statistics that are useful to examine if seasonal level shift in a</span><span style="font-family:Verdana;">n</span><span style="font-family:Verdana;"> SARFIMA-GARCH model </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> also considered.</span>
