Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting

Quality & Quantity - Predicting the number of foreign tourists is significant for governments in devising development policies for tourism demand. Time series related to tourism often feature...     

Restarting a forecasting system when demand suddenly changes

Exponential smoothing is commonly used in automatic forecasting systems. However, when only a small amount of historical data is relevant to future demands, the ad hoc startup methods used in exponential smoothing produce unexpected results. With large data sets, an exponentially smoothed average implicitly weights the data in a declining manner, similar to discounting. This pattern is important in that it minimizes a measure of forecast error. However, restarting with limited data distorts the weighting pattern. A new technique, termed the declining alpha method, is presented and shown to preserve the exponential weight pattern. The key is a formula that changes the smoothing constant each period. Examples are given to illustrate the method and contrast it to other startup techniques.     

Estimation of food demand in Turkey: method of an almost ideal demand system

In this study, data of the household income and consumption expenditure surveys conducted by the Turkish Statistical Institute for 1994 and 2003 years were used; income, price, and cross price elasticities under six aggregated product groups were estimated within the framework of the an almost ideal demand system approach for food expenditures; and estimation of household consumers’ food demand in Turkey was analyzed. According to the findings obtained, it was established that a price-bound change would appear in the food demand, and elasticities were calculated. Expenditures by product groups and price elasticities were obtained, and the product groups were aggregated as bread and cereals; meat, fish, and poultry; milk and dairy products, oil and egg; vegetables and fruits; various fast food and alcoholic and non-alcoholic beverages.     

The use of advance order data in demand forecasting

This paper uses advance order data and historical demand data from a manufacturing shop and from a service operation to develop and test a forecasting methodology for predicting customer demand over a forecast horizon. The proposed methodology uses simple linear regression to model the relationship between a total demand ratio and a partial demand ratio. Comparison of the proposed model to a standard regression approach and a commonly used multiplicative model showed that the proposed model exhibited the greatest forecast accuracy.     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

Short interval forecasting of emergency phone call (911) work loads

There has been an increasing emphasis over the last 5 to 10 years to improve productivity in the Service Sector of the U.S. economy. Much of the improvement obtained by these managers has come about through better scheduling of the work force in these organizations. Effective scheduling of this personnel requires good estimates of demand, which may exhibit substantial variations between days for certain times of the year. The Indianapolis Police Department (IPD) Communications area is one such organization that exhibits varying workloads and is interested in improving staff scheduling of dispatch operators.This article explores the use of six different forecasting techniques for predicting daily emergency call workloads for the IPD's communications area. Historical call volume data are used to estimate the model parameters. A hold-out sample of five months compares forecasts and actual daily call levels. The forecast system utilizes a rolling horizon approach, where daily forecasts are made for the coming month from the end of the prior month. The forecast origin is then advanced to the end of the month, where the current month's actual call data are added to the historical database, new parameters are estimated, and then the next month's daily estimates are generated. Error measures of residual standard deviation, mean absolute percent error, and bias are used to measure performance. Statistical analyses are conducted to evaluate if significant differences in performance are present among the six models.The research presented in this article indicates that there are clearly significant differences in performance for the six models analyzed. These models were tailored to the specific structure and this work suggests that the short interval forecasting problems faced by many service organizations has several structural differences compared to the typical manufacturing firm in a made-to-stock environment. The results also suggests two other points. First, simple modeling approaches can perform well in complex environments that are present in many service organizations. Second, special tailoring of the forecasting model is necessary for many service firms. Historical data patterns for these organizations tend to be more complex than just trend and seasonal elements, which are normally tracked in smoothing models. These are important conclusions for both managers of operating systems and staff analysts supporting these operating systems. The design of an appropriate forecasting system to support effective staff planning must consider the nature, scope, and complexity of these environments.     

An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system

An Unobserved Components (UC) Model based on an enhanced version of the Dynamic Harmonic Regression model, including new multi-rate and modulated cycle procedures, is used to develop a customised package for forecasting and signal extraction applied to hourly telephone call numbers made to Barclaycard plc. service centres, with a forecasting horizon of up to several weeks in advance. The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the ^® computing environment. The forecasting performance is evaluated comprehensively in comparison with the well-known seasonal ARIMA approach.     

Testing a method to estimate county-level demand for educational attainment

Recent changes in federal support for local workforce training programs have created an increased need for methods of assessing demand for educational attainment in sub-state regions. This research extends current non-survey methods for estimating demand for educational attainment at the county level. It then evaluates the non-survey method on theoretical grounds, and compares survey and non-survey estimates for two West Virginia counties. We found significant differences between survey and non-survey results and conclude that current non-survey methodology will not consistently provide reasonably accurate estimates. We thus propose adjustments to this methodology that should improve its accuracy. Many, if not most, jurisdictions do not have the expertise or funds to carry out periodic employment surveys. Many local policymakers would therefore benefit from development of an inexpensive, straightforward non-survey methodology that provides reasonably accurate information on local employer demand for labor force characteristics such as educational attainment.     

Improving demand forecasting in the air cargo handling industry: a case study

Air transportation plays a crucial role in the agile and dynamic environment of contemporary supply chains. This industry is characterised by high air cargo demand uncertainty, making forecasting extremely challenging. An in-depth case study has been undertaken in order to explore and untangle the factors influencing demand forecasting and consequently to improve the operational performance of an air cargo handling company. It has been identified that in practice, the demand forecasting process does not provide the necessary level of accuracy, to effectively cope with the high demand uncertainty. This has a negative impact on a whole range of air cargo operations, but especially on the management of the workforce, which is the most expensive resource in the air cargo handling industry. Besides forecast inaccuracy, a range of additional hidden factors that affect operations management have been identified. A number of recommendations have been made to improve demand forecasting and workforce management.     

Regional forecasting of the demand for fossil fuels by electric utilities in the United States

The possibility of interfuel substitution in the generation of electrical energy exists. Given this fact, a demand model for various fossil fuels by electric utilities in the United States is developed. Using regional data, the results suggest that the responsiveness of the demand for coal, residual fuel oil, distillate fuel oil, and natural gas by electric utilities to relative price changes is significant. In a forecasting setting, the demand model performs remarkably well when actual and forecast values for 1979 are compared.     

A hybrid system-identification method for forecasting telecommunications product demands

A crucial challenge for telecommunications companies is how to forecast changes in demand for specific products over the next 6 to 18 months—the length of a typical short-range capacity-planning and capital-budgeting planning horizon. The problem is especially acute when only short histories of product sales are available. This paper presents a new two-level approach to forecasting demand from short-term data. The lower of the two levels consists of adaptive system-identification algorithms borrowed from signal processing, especially, Hidden Markov Model (HMM) methods [Hidden Markov Models: Estimation and Control (1995) Springer Verlag]. Although they have primarily been used in engineering applications such as automated speech recognition and seismic data processing, HMM techniques also appear to be very promising for predicting probabilities of individual customer behaviors from relatively short samples of recent product-purchasing histories. The upper level of our approach applies a classification tree algorithm to combine information from the lower-level forecasting algorithms. In contrast to other forecast-combination algorithms, such as weighted averaging or Bayesian aggregation formulas, the classification tree approach exploits high-order interactions among error patterns from different predictive systems. It creates a hybrid, forecasting algorithm that out-performs any of the individual algorithms on which it is based. This tree-based approach to hybridizing forecasts provides a new, general way to combine and improve individual forecasts, whether or not they are based on HMM algorithms. The paper concludes with the results of validation tests. These show the power of HMM methods to forecast what individual customers are likely to do next. They also show the gain from classification tree post-processing of the predictions from lower-level forecasts. In essence, these techniques enhance the limited techniques available for new product forecasting.     

A new structural break model,with an application to Canadian inflation forecasting

This paper develops an efficient approach to modelling and forecasting time series data with an unknown number of change-points. Using a conjugate prior and conditioning on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. Furthermore, the conjugate prior is modeled as hierarchical in order to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients, or both. The regime duration can be modelled as a Poisson distribution. A new, efficient Markov chain Monte Carlo sampler draws the parameters from the posterior distribution as one block. An application to a Canadian inflation series shows the gains in forecasting precision that our model provides.     

A method for estimating the timing interval in a linear econometric model, with an application to Taylor's model of staggered contracts

This paper describes and implements a procedure for estimating the timing interval in any linear econometric model. The procedure is applied to Taylor's model of staggered contracts using annual averaged price and output data. The fit of the version of Taylor's model with serially uncorrelated disturbances improves as the timing interval of the model is reduced.     

全球卫星定位系统观测记录及查询新方法

文章介绍了用Delphi创建数据库的优点,并详细阐述了利用Delphi创建观测数据库的方法和步骤。     

Developing an instrument to assess seaport effectiveness in service delivery

The purpose of the research was to develop a standard instrument that can accurately and reliably measure port service effectiveness performance for port authorities. The study population was customers and users of container ports in the USA and Canada. We have named the instrument SEAPORT (Seaport Effectiveness Assessment for PORT managers) and it is designed to be used as a standalone measurement tool as well as input into the strategic management decisions of port managers. The paper details the methodology used in surveying three port user groups and explains the development of the construct for performance measurement by each, using variance inflation factors to finalise the formative construct components. The paper also discusses the potential future deployment of the instrument.     

Estimating consumer preferences using market data—an application to us automobile demand

This paper explores the possibility of using market data to identify consumer preferences. A utility function composed of ‘homogeneous’ characteristics and goods-specific effects is used as a basic link between the goods space and the characteristics space. The functional form for the hedonic price equation, the data requirements and issues of measurement errors for estimating demand and supply of characteristics are discussed. We illustrate the methodology by considering the US automobile demand using 1969–86 data compiled from Consumer Reports and Ward's Automotive Yearbook.     

Using qualitative approach to forecasting regime switches

Drastic changes (named regime switches) often exist in economic and financial time series causing the forecasting of time series difficult. Hence, we need robust models to detect and forecast the regime switches. Most previous studies apply quantitative methods to forecast time series and regime switches. Contrast to these studies, this study attempts a novel approach to use a qualitative method to forecast regime switches. Fuzzy set/qualitative comparative analysis (fsQCA), based on fuzzy set and logic theory, yields the relationships between antecedent combinations and outcome. Studies support fsQCA analysis is more proper to reflect the real situations. Hence, this study uses fsQCA to analyze the autoregressive relationships of the upward and downward regime switches in the in-sample data. Then, the relationships are used to forecast the regime switches in the out-of-sample data. Taiwan Capitalization Weighted Stock Index is taken as the data for analysis. The empirical results show that fsQCA provides strong predictive validities.     

Out-of-sample tests of forecasting accuracy: an analysis and review

In evaluations of forecasting accuracy, including forecasting competitions, researchers have paid attention to the selection of time series and to the appropriateness of forecast-error measures. However, they have not formally analyzed choices in the implementation of out-of-sample tests, making it difficult to replicate and compare forecasting accuracy studies. In this paper, I (1) explain the structure of out-of-sample tests, (2) provide guidelines for implementing these tests, and (3) evaluate the adequacy of out-of-sample tests in forecasting software. The issues examined include series-splitting rules, fixed versus rolling origins, updating versus recalibration of model coefficients, fixed versus rolling windows, single versus multiple test periods, diversification through multiple time series, and design characteristics of forecasting competitions. For individual time series, the efficiency and reliability of out-of-sample tests can be improved by employing rolling-origin evaluations, recalibrating coefficients, and using multiple test periods. The results of forecasting competitions would be more generalizable if based upon precisely described groups of time series, in which the series are homogeneous within group and heterogeneous between groups. Few forecasting software programs adequately implement out-of-sample evaluations, especially general statistical packages and spreadsheet add-ins.