Deep learning approach and topic modelling for forecasting tourist arrivals

Online review data attracts the attention of researchers and practitioners in various fields, but its application in tourism is still limited. The social media data can finely reflect tourist arrivals forecasting. Accurate prediction of tourist arrivals is essential for tourism decision-makers. Although current studies have exploited deep learning and internet data (especially search engine data) to anticipate tourism demand more precisely, few have examined the viability of using social media data and deep learning algorithms to predict tourism demand. This study aims to find the key topics extracted from online reviews and integrate them into the deep learning model to forecast tourism demand. We present a novel forecasting model based on TripAdvisor reviews. Latent topics and their associated keywords are captured from reviews through Latent Dirichlet Allocation (LDA), These generated features are then employed as an additional feature into the deep learning (DL) algorithm to forecast the monthly tourist arrivals to Hong Kong from USA. We used machine learning models, artificial neural networks (ANNs), support vector regression (SVR), and random forest (RF) as benchmark models. The empirical results show that the proposed forecasting model is more accurate than other models, which rely only on historical data. Furthermore, our findings indicate that integration of the topics extracted from social media reviews can enhance the prediction

Forecasting Tourist Arrivals Using a Combination of Long Short-Term Memory and Fourier Series

The sector that contributes most to the nation's economy nowadays is tourism. Policymakers, decision-makers, and organisations involved in the tourist sector can use tourism demand forecasting to gather important information for planning and making important decisions. However, it is difficult to produce an accurate forecast because tourism data is critical, especially when a periodic pattern, such as seasonality, trends, and non-linearity, is present in a dataset. In this research, we present a hybrid modelling approach for modelling tourist arrivals time series data that combines the long short-term memory (LSTM) with the Fourier series method. This method is proposed to capture the components of seasonality and trend in the data set. Various single models, such as ARIMA and LSTM, as well as a modified ARIMA model based on Fourier series, are evaluated to confirm the suggested model's accuracy. The efficiency of the proposed model is compared using monthly tourism arrivals data from Langkawi Island, which has a notable pattern and seasonality. The findings reveal that the proposed model is more reliable than the other models in forecasting tourist arrivals series

EXPERT SYSTEM TO GUIDE USERS OF THE TOURIST CORRIDOR OF THE PROVINCES OF JAEN, SAN IGNACIO AND UTCUBAMBA IN CAJAMARCA, PERU

Purpose:  Develop a rule-based expert system to guide the users of the tourist corridor of the provinces of Jaén, San Ignacio and Utcubamba (Peru).   Theoretical framework:  Hussein and Aqel (2015), developed a rule-based expert system in Jordan to choose the best tour package based on time, budget, and preferences. In Peru, Ramos and Valdivia (2017), proposed an expert system to promote tourism in the Lambayeque region.   Methodology:  To develop the system, the methodology of Nicolás Kemper was used. Tourism experts from the provinces participated in the development of the knowledge base. The evaluation was carried out with an expert different from those who prepared the aforementioned base.   Findings:  In the evaluation, the expert system and the human expert agreed on the recommendation of tourist attractions by 80%. Concluding that this system helps tourists in making decisions about which places to visit in the tourist corridor.   Contributions:  The system helps improve the dissemination of local tourist information. To develop the knowledge base, tourism resources were systematized. New variables can be incorporated into the knowledge base in order to obtain more personalized tourist recommendations.   Originality/value:  This research is innovative because there is no expert system to guide tourists who want to travel to these places; it has social relevance as it helps to boost the local economy.Purpose: Develop a rule-based expert system to guide the users of the tourist corridor of the provinces of Jaén, San Ignacio and Utcubamba (Peru). Theoretical framework: Hussein and Aqel (2015), developed a rule-based expert system in Jordan to choose the best tour package based on time, budget, and preferences. In Peru, Ramos and Valdivia (2017), proposed an expert system to promote tourism in the Lambayeque region. Methodology: To develop the system, the methodology of Nicolás Kemper was used. Tourism experts from the provinces participated in the development of the knowledge base. The evaluation was carried out with an expert different from those who prepared the aforementioned base. Findings: In the evaluation, the expert system and the human expert agreed on the recommendation of tourist attractions by 80%. Concluding that this system helps tourists in making decisions about which places to visit in the tourist corridor. Contributions: The system helps improve the dissemination of local tourist information. To develop the knowledge base, tourism resources were systematized. New variables can be incorporated into the knowledge base in order to obtain more personalized tourist recommendations. Originality/value: This research is innovative because there is no expert system to guide tourists who want to travel to these places; it has social relevance as it helps to boost the local economy

A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics

In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon

A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics

Working paperIn this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon.Preprin

Using social media big data for tourist demand forecasting: A new machine learning analytical approach

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing

Improved mutual information method in combination model selection for forecasting tourist arrival

During the past several decades, a considerable amount of studies has been carried out on finding the highest accurate forecast model. Recently, it has been demonstrated that combining forecasts of individual models can improve forecast performance. Nevertheless, in practice, selecting individual forecast for model combination based on forecast accuracy evaluation might not have extracted all the significant information for the actual output forecast values. Hence, it is advocated to select the optimal individual model from theoretical and experimental aspects that may be able to offer more information to provide a better prediction of combination forecast model. Thus, the mutual information algorithm scaling proposed (MI-S-P) approach is proposed in this study to select the optimal individual model as an input for combination forecast model. Seven individual models and three linear combination methods are applied in this study to evaluate the effectiveness of the MI-S-P approach. The data used in this study is a short term 12 months ahead forecast which includes the monthly data on the top five international tourists arrival entering into Malaysia from the year 2000 to 2013. The results from this study is divided into two main parts, namely in-sample data (fitted model) and out-sample data (forecast model). The analyses show that the in-sample and out-sample values using MI-S-P model has successfully improve forecast accuracy on average by 2% compared to using all of individual forecast combination models. This study concludes that MI-S-P approach can be an alternative way in identifying the right optimal individual model for modelling combination forecast model

Peramalan Jumlah Wisatawan Mancanegara Yang Datang Ke Indonesia Berdasarkan Pintu Masuk Menggunakan Metode Support Vector Machine (SVM)

Pariwisata merupakan salah satu dari beberapa industri besar di dunia dan merupakan faktor penting dalam perkembangan ekonomi global . Perkembangan pariwisata semakin pesat dengan disertai kebutuhan manusia untuk berekreasi yang semakin meningkat. Berbagai sarana dan prasarana penunjang kegiatan pariwisata bermunculan, tumbuh dan berkembang dengan pesat. Pariwisata di Indonesia sendiri merupakan sektor ekonomi yang cukup penting dan menempati urutan ketiga dalam hal penerimaan devisa. Banyaknya potensi kekayaan alam dan budaya yang tersebar secara berlimpah menjadikan setiap daerah Indonesia memiliki objek wisata yang dapat menarik para wisatawan baik lokal maupun mancanegara. Indonesia dikenal sebagai Negara yang memiliki kepulauan terbesar di dunia dengan beragam keindahan alam. Dengan begitu, Indonesia dapat dengan mudah menarik para wisatawan terutama bagi para wisatawan mancanegara yang ingin lebih mengenal Indonesia. Tetapi jumlah wisatawan dapat berubah sewaktu-waktu pada dampak yang dapat diakibatkan oleh suatu kondisi tertentu. Dan dalam upaya untuk meminimalisir jumlah wisatawan yang tidak tentu tersebut, maka dalam penelitian ini akan diramalkan jumlah wisatawan mancanegara yang datang ke Indonesia berdasarkan 6 pintu masuk dari 19 pintu masuk utama. Pada 19 pintu masuk hanya dipilih 6 dikarenakan untuk membatasi penelitian. Dimana hasil peramalan dan prediksi yang akurat dari perkiraan jumlah wisatawan mancanegara di masa depan dapat memberikan strategi yang tepat bagi industri pariwisata. Pada penelitian ini, digunakan metode SVM untuk meramalkan jumlah wisatawan mancanegara yang datang ke Indonesia dari 6 pintu masuk. Pintu masuk dipilih berdasarkan hasil klasterisasi K-means yang dibagi menjadi 3 klaster yakni tinggi, sedang dan rendah. Masing-masing klaster kemudian hanya dipilih dua sebagai contoh yang diambil dari nilai tertinggi dan terendah berdasarkan rata-rata jumlah wisman. Penggunaan SVM sendiri memiliki kelebihan yaitu dapat menangani permasalahan linier dan non-linier. Sehingga dapat dilakukan untuk melakukan peramalan data time series dengan berbagai macam pola yang ada. Selain itu, tidak hanya memprediksi permasalahan non-linier tetapi juga menawarkan akurasi yang cukup baik. Hasil yang diperoleh dari uji coba penelitian ini menunjukkan bahwa model peramalan secara keseluruhan tergolong baik. Rata-rata akurasi dari 6 model memiliki MAPE sekitar 10% dengan nilai terkecil yakni 4.50% pada pintu masuk Ngurah Rai. Selain itu, hasil dari SVM juga memiliki akurasi perubahan arah data atau Directional Change Accuracy (DCA) yang cukup baik. Hal ini dibuktikan dengan hasil rata-rata DCA secara keseluruhan sebesar 62.64% dengan nilai tertinggi yakni 64.01% pada pintu masuk Husein Sastranegara. Dengan adanya informasi tersebut, diharapkan sektor industri pariwisata terkait dapat membentuk suatu kebijakan terhadap peningkatan pelayanan atau fasilitas yang dapat meminimalisir penurunan jumlah wisatawan. ====================================================================== Tourism is one of the few major industries in the world and is an important factor in the development of the global economy. The more rapid development of tourism with accompanying human needs for recreation increases. Various facilities and infrastructure supporting tourism activities emerge, grow and thrive. Tourism in Indonesia itself is a fairly important economic sector and ranks third in terms of foreign exchange earnings. The number of potential natural and cultural wealth are scattered abundantly made in every area of Indonesia has attractions that can attract both local and foreign travelers. Indonesia is known as the country that has the largest archipelago in the world with a variety of natural beauty. That way, Indonesia can easily attract tourists, especially for foreign tourists who want to get to know Indonesia. But the number of tourists may change at any time on the impact that can be caused by a certain condition. And in an effort to minimize the number of tourists who are not necessarily, then in this study will be predicted the number of foreign tourists who come to Indonesia based on 6 entrances of the 19 main entrance. At 19 entrances only 6 were chosen due to restricting the research. Where forecasting results and accurate prediction of the estimated number of foreign tourists in the future can provide appropriate strategies for the tourism industry. In this research, SVM method is used to predict the number of foreign tourists who come to Indonesia from 6 entrances. The entrance is selected based on the cluster of K-means cluster which is divided into 3 clusters ie high, medium and low. Each cluster is then selected only two as an example taken from the highest and lowest values based on the average number of foreign tourists. The use of SVM itself has the advantage of being able to handle linear and non-linear problems. So it can be done to forecast time series data with a variety of existing patterns. In addition, not only predict non-linear problems but also offer fairly good accuracy. The results obtained from this research trial show that the overall forecasting model is good. The average accuracy of 6 models has a MAPE of about 10% with the smallest value of 4.50% at the entrance of Ngurah Rai. In addition, the results of SVM also have a pretty good accuracy of Directional Change Accuracy (DCA). This is evidenced by the overall average DCA result of 62.64% with the highest score of 64.01% at the entrance of Husein Sastranegara. Given this information, it is expected that the relevant tourism industry sector can form a policy towards the improvement of services or facilities that can minimize the decline in the number of tourists

New hybrid multi-criteria decision-making DEMATELMAIRCA model: sustainable selection of a location for the development of multimodal logistics centre

The paper describes the application of a new multi-criteria decision-making (MCDM) model, MultiAtributive Ideal-Real Comparative Analysis (MAIRCA), used to select a location for the development of a multimodal logistics centre by the Danube River. The MAIRCA method is based on the comparison of theoretical and empirical alternative ratings. Relying on theoretical and empirical ratings the gap (distance) between the empirical and ideal alternative is defined. To determine the weight coefficients of the criteria, the DEMATEL method was applied. In this paper, through a sensitivity analysis, the results of MAIRCA and other MCDM methods – MOORA, TOPSIS, ELECTRE, COPRAS and PROMETHEE – were compared. The analysis showed that a smaller or bigger instability in alternative rankings appears in MOORA, TOPSIS, ELECTRE and COPRAS. On the other hand, the analysis showed that MAIRCA and PROMETHEE offer consistent solutions and have a stable and wellstructured analytical framework for ranking the alternatives. By presenting a new method MCDM expands the theoretical framework of expertise in the field of MCDM. This enables the analysis of practical problems with new methodology and creates a basis for further theoretical and practical upgrade