Forecasting tourism demand is a crucial issue in the tourism industry and is generally seen to be one of the most complex functions of tourism management. With the accurate forecasted trends and patterns that indicate the sizes, directions and characteristics of future international tourist flows, the government and private sectors can have a well-organized tourism strategy and provide a better...

In the Summer of 2008 we have upgraded STAMP to version 8.10 which is the current release version. The new items in version 8.10 are relatively small. The forecasting dialog allows the forecasting of the unobserved components as well as the future observations. More importantly, various errors have been removed from the program and some improvements have been introduced. Most notably, the batch...

In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models ...

In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison betw...

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...

background and objectives: efficient cost management in hospitals’ pharmaceutical inventories have thepotential to remarkably contribute to optimization of overall hospital expenditures. to this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. while the linear methods are frequently used for forecasting purposes chiefly due to their sim...

The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under va...

This paper uses half-hourly electricity demand data in South Australia as an empirical study of nonparametric modeling and forecasting methods for prediction from half-hour ahead to one year ahead. A notable feature of the univariate time series of electricity demand is the presence of both intraweek and intraday seasonalities. An intraday seasonal cycle is apparent from the similarity of the d...