Forecasting tourism demand using search query data: A hybrid modelling approach

2019 ◽  
Vol 25 (3) ◽  
pp. 309-329 ◽  
Author(s):  
Long Wen ◽  
Chang Liu ◽  
Haiyan Song
Keyword(s):  
Linear Models ◽  
Moving Average ◽  
Mainland China ◽  
Network Models ◽  
Tourism Demand ◽  
Search Query ◽  
Neural Network Models ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural Network Models ◽  
Component Models

Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

Neural Models for Rainfall Forecasting

2010 ◽  
pp. 353-369
Author(s):  
A. Moreno ◽  
E. Soria ◽  
J. García ◽  
J. D. Martín ◽  
R. Magdalena
Keyword(s):  
Vegetation Cover ◽  
Linear Models ◽  
Moving Average ◽  
Network Models ◽  
Climatic Data ◽  
Neural Models ◽  
Rainfall Forecasting ◽  
Precipitation Regime ◽  
Neural Network Models ◽  
The Status

This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The approaches considered for rainfall forecasting include classical Auto-Regressive Moving-Average with Exogenous Inputs (ARMAX) models and Artificial Neural Networks (ANN), the so-called Multilayer Perceptron (MLP), in particular. The use of neural models is proven to be an adequate mathematical prediction tool in this problem due the non-linearity of the problem. These models enable us to predict, with one month foresight, the general rainfall dynamics, with average errors of 44 mm (RMSE) in a test series of 4 years with a rainfall standard deviation equal to 73 mm. Also, the sensitivity analysis in the neural network models reveals that observations in the status of the vegetation cover in previous months have a predictive power greater than other considered variables. Linear models yield average results of 55 mm (RMSE) although they need a large number of error terms (12) to obtain acceptable models. Nevertheless, they provide means for assessing the seasonal influence of the precipitation regime with the aid of linear dummy regression parameters, thereby offering an immediate interpretation (e.g. coherent maps) of the causality between vegetation cover and rainfall.

scholarly journals Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Ning-Kang Pan ◽  
Chunwan Lv
Keyword(s):  
Neural Network ◽  
Policy Making ◽  
Energy Requirement ◽  
Moving Average ◽  
Network Models ◽  
Primary Energy ◽  
Backpropagation Neural Network ◽  
Neural Network Models ◽  
Autoregressive Integrated Moving Average ◽  
Primary Energy Requirement

Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

scholarly journals Forecasting tourist arrivals at attractions: Search engine empowered methodologies

2018 ◽  
Vol 25 (3) ◽  
pp. 425-447 ◽  
Author(s):  
Katerina Volchek ◽  
Anyu Liu ◽  
Haiyan Song ◽  
Dimitrios Buhalis
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Network Models ◽  
Autoregressive Moving Average ◽  
Data Sampling ◽  
Neural Network Models ◽  
Explanatory Variables ◽  
Frequency Search ◽  
Artificial Neural Network Models ◽  
Mixed Frequency Data

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

CEO Tenure and Debt

2009 ◽  
pp. 330-347
Author(s):  
Jean X. Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Local Governments ◽  
Linear Models ◽  
Nonlinear Models ◽  
Network Models ◽  
Neural Network Models ◽  
Ceo Tenure ◽  
Artificial Neural Network Models ◽  
The Relationship

This chapter proposes nonlinear models using artificial neural network models to study the relationship between chief elected official (CEO) tenure and debt. Using Higher Order Neural Network (HONN) simulator, this study analyzes debt of the municipalities as a function of population and CEO tenure, and compares the results with that from SAS. The linear models show that CEO tenure and the amount of debt vary inversely. Specifically, a longer length of CEO tenure leads to a decrease in debt, while a shorter tenure leads to an increase in debt. This chapter shows nonlinear model generated from HONN out performs linear models by 1%. The results from both models reveal that CEO tenure is negatively associated with the level of debt in local governments.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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