Forecasting occupancy rate with Bayesian compression methods

The curse of dimensionality is a challenge that researchers often face when dealing with large Vector Autoregressions (VARs). Different approaches have been proposed in the literature to address this issue. In this paper, we propose a new method based on the idea of compressed regression. In particular, we introduce two novel nonlinear compressed VARs to forecast the occupancy rate of hotels that compete within a narrow geographical area. We make the models more flexible through the introduction of neural networks, and compare their performance against several competing models. The empirical results show that the new compressed VARs outperform all other models, and their accuracy is preserved across nearly all forecast horizons from 1 to 36 months.

[1]  Linda Canina,et al.  Agglomeration Efects and Strategic Orientations: Evidence From The U.S. Lodging Industry , 2005 .

[2]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .

[3]  Mustafa Akal,et al.  Forecasting Turkey's tourism revenues by ARMAX model , 2004 .

[4]  M. Girolami,et al.  Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[5]  Haiyan Song,et al.  Bayesian models for tourism demand forecasting. , 2006 .

[6]  Density forecasting for tourism demand , 2016 .

[7]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[8]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[9]  Emmanuel Sirimal Silva,et al.  Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques , 2015 .

[10]  S. Sundararajan,et al.  Predictive Approaches for Choosing Hyperparameters in Gaussian Processes , 1999, Neural Computation.

[11]  Tao Chen,et al.  Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index , 2018, Tourism Management.

[12]  Bing Pan,et al.  Forecasting hotel room demand using search engine data. , 2012 .

[13]  A. Assaf,et al.  Internationalization and hotel performance , 2016 .

[14]  T. Lockyer.  The perceived importance of price as one hotel selection dimension , 2005 .

[15]  Athanasius Zakhary,et al.  Forecasting hotel arrivals and occupancy using Monte Carlo simulation , 2011 .

[16]  Mike G. Tsionas,et al.  Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model , 2019 .

[17]  Dongchu Sun,et al.  Bayesian stochastic search for VAR model restrictions , 2008 .

[18]  Cheng-Hua Wang,et al.  Support vector regression with genetic algorithms in forecasting tourism demand , 2007 .

[19]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[20]  David J. C. MacKay,et al.  Comparison of Approximate Methods for Handling Hyperparameters , 1999, Neural Computation.

[21]  M. Hallin,et al.  The Generalized Dynamic-Factor Model: Identification and Estimation , 2000, Review of Economics and Statistics.

[22]  Zvi Schwartz,et al.  Hotel daily occupancy forecasting with competitive sets: a recursive algorithm , 2016 .

[23]  P. Mattana,et al.  A SVECM Analysis of the Relationship between International Tourism Arrivals, GDP and Trade in Italy , 2013 .

[24]  A. O'Hagan,et al.  Curve Fitting and Optimal Design for Prediction , 1978 .

[25]  Samuel E. Bodily,et al.  A test of space-time arma modelling and forecasting of hotel data , 1990 .

[26]  Martin Feldkircher,et al.  Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis , 2012 .

[27]  Chi Kin Chan,et al.  Tourism forecast combination using the CUSUM technique. , 2010 .

[28]  Giorgio E. Primiceri.  Time Varying Structural Vector Autoregressions and Monetary Policy , 2002 .

[29]  M. Hallin,et al.  Dynamic factor models with infinite-dimensional factor spaces: One-sided representations , 2013 .

[30]  Forecasting Tourism-Generated Employment: The Case of Denmark , 2004 .

[31]  Bing Pan,et al.  Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data , 2014 .

[32]  Irem Önder,et al.  Forecasting city arrivals with Google Analytics , 2016 .

[33]  David B. Dunson,et al.  Bayesian Compressed Regression , 2013, ArXiv.

[34]  Joshua C. C. Chan,et al.  Stochastic Model Specification Search for Time-Varying Parameter VARs , 2014 .

[35]  H. White,et al.  Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.

[36]  A. Mattila,et al.  Relationships between Hotel Room Pricing, Occupancy, and Guest Satisfaction: A Longitudinal Case of a Midscale Hotel in the United States , 2003 .