The International Journal of Forecasting (IJF) is a leading journal in the field of Forecasting and is an official publication of the International Institute of Forecasters. There will be special issues of IJF on Tourism Forecasting and Forecasting with artificial neural networks and computational intelligence.
Tourism forecasting has been of great interest to both academics and practitioners for the following reasons. Firstly, the success of many businesses depends largely or totally on the state of tourism demand, and ultimate management failure is quite often due to the failure to meet the market demand. Therefore, accurate forecasts of tourism demand are essential for efficient planning by tourism-related businesses, particularly given the perishable nature of the tourism product. Secondly, tourism investment, especially investment in destination infrastructures requires long-term financial commitments and the sunk costs can be very high if the investment projects fail to fulfill their designed capacities. As a result, the prediction of long-term demand for tourism related infrastructures often forms an important part of the investment project appraisals. Thirdly, government macroeconomic policies largely depend on the relative importance of individual sectors within a destination. Hence, accurate forecasts of demand in the tourism sector of the economy will help the destination governments in formulating and implementing appropriate medium to long term tourism strategies. Last but not least, the demand for tourism in a particular destination is one of the key factors that
determines the destination's competitiveness, therefore accurate assessment of future tourism demand in the destination will help the destination to position itself on the world market in order to compete with its competing destinations.
Given the importance of tourism forecasting for both the public and private sectors, the past 20 years have witnessed great advances in tourism forecasting research in terms of the diversity of research
focuses, depth of theoretical foundations, and advances in forecasting methods. This special issue aims to publish recent developments in tourism forecasting research. In particular, researchers and
practitioners will be invited to submit their latest work to this special issue for consideration for publication. Possible areas to be included in this special issue are tourism forecasting competition,
tourism forecast combination, judgmental forecasting, seasonality and tourism forecasting, applications of neural networks and advanced time series approaches in tourism forecasting.
If you are interested in contributing to this special issue, please submit a 3-page abstract to both editors of the special issue for an initial assessment. A quick response will be given regarding the suitability of the paper for the special issue. The deadline for submission of full papers is 1 December 2008 and we are aiming for the special issue to be published by the end of 2009. All papers submitted will go through a double blind review process and only those papers that meet the requirements of the International Journal of Forecasting would be accepted for publication.
Please submit your initial abstract electronically to both editors listed below:
School of Hotel and Tourism Management
The Hong Kong Polytechnic University
Hung Hom, Kowloon
Email address: email@example.com
Rob J Hyndman
Department of Econometrics & Business Statistics,
Monash University, VIC 3800, Australia
Email address: firstname.lastname@example.org
Forecasting with artificial neural networks and computational intelligence
The last 20 years of research have produced more than 5000 publications on artificial neural networks (NN) for predictive modelling across various disciplines. However, while NN and other methods of computational intelligence (CI) are firmly established in automatic control and classification problems, they have not received the same level of attention in time series forecasting. Many of the optimistic publications indicating a competitive or even superior performance of NNs have focussed on theoretical development of novel paradigms, or extensions to existing methods, architectures, and training algorithms, but have lacked a valid and reliable evaluation of the empirical evidence of their performance. Similarly, only a few publications have attempted to develop a thorough methodology on how to model NNs under specific conditions, limiting the modelling process of NNs to a heuristic and ad-hoc 'art' of hand-tuning individual models, rather than a scientific approach following a replicable methodology and modelling process. As a consequence, NNs have not yet been empirically validated as a forecasting method in many areas of forecasting, despite the theoretical advances.
To explore this gap between academic attention, theoretical prowess and empirical performance we invite contributions to a special issue of the International Journal of Forecasting (IJF) dedicated to evaluating the evidence on forecasting with NN and CI-methods.
Papers for this special issue should focus on novel techniques, methods, methodologies and applications from the computational intelligence domain, with particular emphasis on neural networks, within all aspects of forecasting. Particular emphasis will be placed on applied or applicable work that provides valid and reliable evidence on the performance of the methods and the development of robust methodologies based upon rigorous evaluation, rather than purely theoretical contributions. Contributions of contenders that have contributed to one of the recent forecasting competitions dedicated to NN and CI-methods (ESTSP'07, ESTSP'08, NN3 and NN5) are particularly encouraged. Due to the single-time origin design of these competitions, the authors are encouraged to obtain the complete datasets and rerun experiments for their papers, in order to obtain representative out-of-sample results across multiple origins and error measures in comparison to established statistical benchmark methods, adhering to the best-practices set out in discussions in the IJF (see e.g. Tashman (2000) Out-of-sample tests of forecasting accuracy – an analysis and review, International Journal of Forecasting 16, 437-450; and Adya and Collopy (1998) How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of Forecasting, 17, 481-495).
Each submitted paper will be peer-reviewed in the same manner as other submissions to the IJF. Providing papers fit into the theme of the special issue, quality and originality of the contribution will be the major criteria for each submission. Due to the tight deadlines, any paper for which the outcome of the refereeing process is "major revision" will not be included in the special issue, but may be revised and resubmitted according to the journal's regular process.
Deadline for manuscripts: 15 September, 2008
Preliminary decision to authors: 24 November, 2008
Revision Due: 12 January, 2008
Final Manuscript Due: 16 March, 2008
Authors are encouraged to contact one of the editors to discuss any questions of suitability. Only email submissions will be accepted. Please submit your manuscript to IJF_Special_IssueI@neural-forecasting.com. The submission must be in PDF format. Final manuscripts must be submitted in either MS-Word or LaTeX format for typesetting by the publisher. Manuscripts must be in English and double-spaced throughout. Papers should in general not exceed 6,000 words. All submissions will be peer reviewed. Detailed instructions for authors are at: http://www.forecasters.org/ijf
Prof. Fred Collopy
Information Systems Department
Weatherhead School of Management
Case Western Reserve University
Cleveland, Ohio 44106-7235
Dr. Sven F. Crone
Lancaster University Management School
Research Centre for Forecasting
Lancaster, LA1 4YX
Dr. Amaury Lendasse
Helsinki University of Technology
Laboratory of Computer and Information Science
P.O. Box 5400, FIN-02015 HUT