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The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are provided by interval numbers.

Starting from maximizing the closeness degree of combination forecasting, which is different from minimizing absolute errors, weighted coefficient vectors of combination forecasting methods are obtained. The new concepts of closeness degree for the center and radius of interval numbers sequences are put forward and the optimal interval combination forecasting model is constructed by maximizing the sum of convex combination with closeness degree of interval center and closeness degree of interval radius. The solution to the model is discussed.

The results show that this model can improve the combination forecasting accuracy efficiently compared with that of each single forecasting method.

The method proposed in the paper can be used to forecast future tendency in a wide ranges of fields, such as engineering, economics and management. In particular, the raw data are provided in the form of interval numbers under the uncertain environment.

The combination forecasting model proposed in this paper is based on closeness degree and IOWHA operator, which is a new kind of combination forecasting model with variant weights.

A method is developed for evaluating forecasting models with respect to both error and complexity in forecasting. Several types of forecasting accuracy measures (MSE, MPE, MAPE, Theil′s U‐Statistic and a loss cost function) are examined and the approach is illustrated using short‐term forecasting methods, and weekly and four‐weekly data. The approach can, however, be applied equally to immediate, medium‐ and long‐term forecasting.

Planning, both “operational” and “strategic”, relies on accurate forecasting. Planning in tourism is no less dependent on accurate forecasts. However, tourism demand forecasting has been dominated by the application of regression/econometric techniques. Past studies on the forecasting accuracy of econometric/regression models suggest that forecasts generated by these models are not necessarily superior to forecasts generated by simple time series techniques. Seven time series forecasting techniques were used to generate forecasts of international tourist arrivals from Thailand to Hong Kong. The results confirm that simple techniques may be just as accurate and often more time‐and cost‐effective than more complex ones. Practitioners in the tourism industry may confidently use any of the forecasting techniques demonstrated here for their short‐term planning activities.

The importance of accurate forecasts of tourism demand for managerial decision making is widely recognized (see, for example, Archer 1987), and this study examines the literature on the accuracy of tourism forecasts generated by different forecasting techniques. In fact, although there are many possible forecasting methods, in practice relatively few of these have been used for tourism forecasting.

The purpose of this article is to examine empirically the impact of aggregation on forecasting accuracy; specifically, whether more accurate forecasts are obtained by forecasting a number of disaggregated tourist flows and summing the forecasts to obtain the aggregate forecast, or by summing the disaggregated tourist flows and forecasting the aggregate series directly. On the one hand, it may be easier to produce accurate forecasts from disaggregated series as the latter allow for differing behavioural patterns which may be more readily recognisable and hence easier to model and extrapolate. On the other hand, more aggregate series may be less susceptible to “noise” and therefore easier to forecast.

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non‐seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short‐term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto‐regressive integrated moving average (ARIMA) model).

The Concept of Usefulness Usefulness in forecasting implies two critical issues. Firstly, there is a concern to relate forecasting to its decision making context. This covers the vast majority of forecasting that is actually done although it is possible that a very limited number of instances are genuinely independent of a decision making process.

If there is any one function managers most despise, it is the art of forecasting. By its very nature it concerns guessing the outcome of future events. Do all firms forecast the same? Compares forecasting behavior between industrial product firms and consumer product firms. Examines issues such as who does the forecasting, the frequency of forecasts, and the areas in which forecasts are made. Assesses the results gained from the forecasting effort and examines significant differences in forecasting behavior.

Forecasting is an integral part of all business planning, and forecasting the outlook for housing is of interest to many firms in the housing construction sector. This research measures the performance of a number of industry forecasting bodies; this is done to provide users with an indicator of the value of housing forecasting undertaken in Australia. The accuracy of housing commencement forecasts of three Australian organisations – the Housing Industry Association (HIA), the Indicative Planning Council for the Housing Industry (IPC) and BIS‐Shrapnel – is examined through the empirical analysis of their published forecasts supplemented by qualitative data in the form of opinions elicited from several industry “experts” employed in these organisations. Forecasting performance was determined by comparing the housing commencement forecast with the actual data collected by the Australian Bureau of Statistics on an ex‐post basis. Although the forecasts cover different time periods, the level of accuracy is similar, at around 11‐13 per cent for four‐quarter‐ahead forecasts. In addition, national forecasts are more accurate than forecasts for individual states. This is the first research that has investigated the accuracy of both private and public sector forecasting of housing construction in Australia. This allows users of the information to better understand the performance of various forecasting organisations.

The purpose of this paper is to propose an occurrence-based model to improve the forecasting of regime switches so as to assist decision making.

This paper proposes a novel model where occurrences of relationships are taken into account when forecasting. Taiwan Stock Exchange Capitalization Weighted Stock Index is taken as the forecasting target.

Due to the consideration of occurrences of relationships in forecasting, the out of sample forecasting is improved.

The proposed model can be applied to forecast other time series for regime switches. In addition, it can be integrated with other time series models to improve forecasting performance.

The empirical results show that the proposed model can improve the forecasting performance.