Daily tourism volume forecasting for tourist attractions

Bi, Jian-Wu; Liu, Yang; Li, Hui

doi:10.1016/j.annals.2020.102923

Cited by 120 publications

(92 citation statements)

References 53 publications

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“…In this dataset, there are 12,589 records in the testing data and 113,297 records in the training data. Additionally, the same water quality dataset is predicted with the long short-term memory (LSTM) (Bi, Liu & Li, 2020) and the RBFNN models (Moradi et al, 2020), and the prediction results are compared with those of the enhanced semi-naive Bayesian prediction model. To quantitatively represent the prediction effects of the different algorithms, the root mean square error (RMSE) (Hyndman & Koehler, 2006), mean absolute percentage error (MAPE) (de Myttenaere et al, 2016) and mean absolute error (MAE) (Willmott & Matsuura (2005)) are used as error functions; they are described in Eqs.…”

Section: Results and Validation Single Pasture Prediction Evaluationmentioning

confidence: 99%

A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

Sun

Fan

2021

PeerJ Computer Science

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A challenge of achieving intelligent marine ranching is the prediction of dissolved oxygen (DO). DO directly reflects marine ranching environmental conditions. Through accurate DO predictions, timely human intervention can be made in marine pasture water environments to avoid problems such as reduced yields or marine crop death due to low oxygen concentrations in the water. We use an enhanced semi-naive Bayes model for prediction based on an analysis of DO data from marine pastures in northeastern China from the past three years. Based on the semi-naive Bayes model, this paper takes the possible values of a DO difference series as categories, counts the possible values of the first-order difference series and the difference series of the interval before each possible value, and selects the most probable difference series value at the next moment. The prediction accuracy is optimized by adjusting the attribute length and frequency threshold of the difference sequence. The enhanced semi-naive Bayes model is compared with LSTM, RBF, SVR and other models, and the error function and Willmott’s index of agreement are used to evaluate the prediction accuracy. The experimental results show that the proposed model has high prediction accuracy for DO attributes in marine pastures.

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Section: Results and Validation Single Pasture Prediction Evaluationmentioning

confidence: 99%

A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

Sun

Fan

2021

PeerJ Computer Science

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“…The authors of Reference [33] use the LSTM networks that can incorporate multivariate time series data including historical tourism volume data, search engine data and weather data. The model is proposed for forecasting the daily tourism volume of tourist attractions.…”

Section: Related Workmentioning

confidence: 99%

“…Neural networks usage to analyze and predict the behaviour of tourists. The authors of References [19,28] use convolutional neural networks for image analysis, References [22,[29][30][31]33] work with LSTM networks that allow memorizing previous states, Reference [27] describes recurrent networks for analyzing tourist descriptions of attractions, self-organizing maps [23,25] provide information clustering, the authors of References [16,21,30,31] use combinations of neural networks to improve prediction results. Neural networks, as a rule, work more accurately than similar models, but they require a large amount of data for correct training and revealing hidden dependencies in the provided data, and for each task the volume depends on the type of the task itself (prediction of time events requires more data than classification ) and the number of certain input parameters (the more parameters, the more data is needed to identify dependencies between them).…”

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confidence: 99%

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

Mikhailov

Kashevnik

2020

Future Internet

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The tourism industry has been rapidly growing over the last years and IT technologies have had a great affect on tourists as well. Tourist behaviour analysis has been the subject of different research studies in recent years. This paper presents the digital pattern of life concept which simplifies the tourist behaviour models’ construction and usage. The digital pattern of life defines the general concepts of tourist behaviour, connects the tourist and the digital world and allows us to track behaviour changes over time. A literature review of the current state of the research in selected fields is performed for identifying the existing problems. The case studies of behaviour analysis based on classification, clustering and time series events behaviour models are shown. An ontological approach and artificial neural networks are used during behaviour model construction, training and evaluation. The gathered results can be used by smart tourism service developers and business stakeholders.

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“…In this paper, the multilayer perceptron regressor (MPR) and support vector regression (SVR) algorithms provided by Scikit-learn are chosen to predict the dissolved oxygen time series of the marine pastures. In addition, the same dataset is predicted using the RBFNN, long short-term memory (LSTM) and the autoregressive integrated moving average with exogenous variables (ARIMAX) algorithms [42]. The prediction results are compared with those of the proposed enhanced NB model.…”

Section: Comparison Of the Effects Of Different Predictive Modelsmentioning

confidence: 99%

An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture

Sun

Yang

2020

IEEE Access

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Daily tourism volume forecasting for tourist attractions

Cited by 120 publications

References 53 publications

A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture

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