Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting

By R. Law. 2000

358 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/c7c127ddcf3d14a50d9047a95d2308660e848a34

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In Management Science and Operations Research

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Citations in the corpus

Modelling and Forecasting Australian Domestic Tourism (2006, background): “Law (2000), Burger, Dohnal, Kathrada, and Law (2001), Lim and McAleer (2001b) and Cho (2001, 2003) directly implement exponential smoothing methods to forecast tourism demand.”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, neutral): “The importance of accurate tourism demand forecasting has been already established in the literature since the 1980s, especially given the perishable nature of tourism (see, for example Uysal and O’Leary, 1986; Law and Au, 1999; Law, 2000).”
Recent Developments in Econometric Modeling and Forecasting (2005, methodology): “7 Law & Au (1999) A: 77-97 Japan-Hong Kong TA RC P ER ME HR FNN Linear Naive 1 SES MA SR Z r”
Support vector regression with genetic algorithms in forecasting tourism demand (2007, methodology): “Law (2000) extended the applicability of neural networks in tourism demand forecasting by incorporating the back-propagation learning process into 0261-5177/$ - see front matter r 2006 Elsevier Ltd.”
Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing (2014, background): “As described in Algorithm 1, all the weights are initialized as small random numbers [10], [7], [16].”
The impact of COVID-19 on tourism sector in India (2020, methodology): “Therefore, many researchers applied the ANN model for prediction and proved that it is a suitable model for prediction irrespective of the data pattern (Cho, 2003; Claveria & Torra, 2014; Höpken et al., 2020; Law, 2000; Palmer et al., 2006).”
Tourism demand modelling and forecasting—A review of recent research (2008, methodology): “Its applications to tourism demand analysis can be seen in Au and Law (2000; 2002) and Law and Au (2000) for tourism shopping, dining and sightseeing expenditure analyses, respectively.”
Hotel Revenue Management – A Critical Literature Review (2011, neutral): “…methods like Delphi (Yüksel, 2007) have found only marginal application, neural networks receive growing attention (e.g. Burger et al., 2001; Law, 2000; Padhi & Aggarwal, 2011; Zakhary, El Gayar & Ahmed, 2010) due to their learning capability, which is the essential characteristic of neural…”
The determinants of inbound tourism to Malaysia: a panel data analysis (2017, background): “, 2005; Narayan, 2004) and marketing expenditures (Crouch et al., 1992; Law, 2000; Papadopoulos & Witt, 1985).”
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model (2012, background): “Associated with each connection is a weight [24].”