Cross-temporal coherent forecasts for Australian tourism

Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.

[1]  F. Petropoulos,et al.  Improving forecasting by estimating time series structural components across multiple frequencies , 2014 .

[2]  John T. Coshall,et al.  A management orientated approach to combination forecasting of tourism demand , 2011 .

[3]  Martin Fluker,et al.  Domestic demand for Indigenous tourism in Australia: understanding intention to participate , 2016, Sustainable Tourism and Indigenous Peoples.

[4]  Robert L. Winkler,et al.  Sensitivity of Weights in Combining Forecasts , 1992, Oper. Res..

[5]  C. Granger,et al.  Handbook of Economic Forecasting , 2006 .

[6]  Rob J Hyndman,et al.  A state space framework for automatic forecasting using exponential smoothing methods , 2002 .

[7]  Fotios Petropoulos,et al.  Another look at forecast selection and combination: Evidence from forecast pooling , 2019, International Journal of Production Economics.

[8]  Nikolaos Kourentzes,et al.  Distributions of forecasting errors of forecast combinations: Implications for inventory management , 2016 .

[9]  Nikolaos Kourentzes,et al.  Demand forecasting with user-generated online information , 2019, International Journal of Forecasting.

[10]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[11]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[12]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[13]  R. Fildes,et al.  Measuring forecasting accuracy : the case of judgmental adjustments to SKU-level demand forecasts , 2013 .

[14]  Fotios Petropoulos,et al.  Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption , 2020, Applied Energy.

[15]  El-Houssaine Aghezzaf,et al.  Temporal Big Data for Tactical Sales Forecasting in the Tire Industry , 2018, Interfaces.

[16]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[17]  Konstantinos Nikolopoulos,et al.  The Tourism Forecasting Competition , 2011 .

[18]  Amir F. Atiya,et al.  Combination of long term and short term forecasts, with application to tourism demand forecasting , 2011 .

[19]  Haiyan Song,et al.  Forecasting turning points in tourism growth , 2018, Annals of Tourism Research.

[20]  Rob J. Hyndman,et al.  Optimal combination forecasts for hierarchical time series , 2011, Comput. Stat. Data Anal..

[21]  Shui Ki Wan,et al.  AGGREGATE VS. DISAGGREGATE FORECAST: CASE OF HONG KONG , 2013 .

[22]  George Athanasopoulos,et al.  Hierarchical forecasts for Australian domestic tourism , 2009 .

[23]  Nikolaos Kourentzes,et al.  Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels? , 2017 .

[24]  Some Nonlinear Exponential Smoothing Models are Unstable , 2006 .

[25]  K. Wallis,et al.  A Simple Explanation of the Forecast Combination Puzzle , 2009 .

[26]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[27]  Fotios Petropoulos,et al.  Forecasting with multivariate temporal aggregation: the case of promotional modelling , 2016 .

[28]  Nikolaos Kourentzes,et al.  Tactical sales forecasting using a very large set of macroeconomic indicators , 2018, Eur. J. Oper. Res..

[29]  Rob J. Hyndman,et al.  Probabilisitic forecasts in hierarchical time series , 2018 .

[30]  Gang Li,et al.  Combination forecasts of international tourism demand , 2011 .

[31]  Rob J. Hyndman,et al.  Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization , 2018, Journal of the American Statistical Association.

[32]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[33]  Rob J Hyndman,et al.  Forecasting with Exponential Smoothing: The State Space Approach , 2008 .

[34]  Examining domestic and international visits in Australia’s Aboriginal tourism , 2018 .

[35]  Fotios Petropoulos,et al.  Forecasting with temporal hierarchies , 2017, Eur. J. Oper. Res..

[36]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .