Deep Learning applied to tourism forecast in Costa del Sol

En los últimos años Deep Learning se ha convertido en uno de los tópicos más importantes a nivel mundial en las áreas de Inteligencia Artificial y sus aplicaciones. Muchas empresas lo ven como una herramienta potencial para mejorar su productividad, predecir eventos futuros y prever posibles ganancias y pérdidas. Deep Learning ha tenido mucho éxito en tareas de reconocimiento de imágenes, textos y series temporales, en algunos casos con un rendimiento superior al nivel humano, por lo que se ha convertido en la tecnología dominante dentro de la inteligencia artificial. Por otro lado, la información sobre las búsquedas en Internet ya es accesible desde varias fuentes, por ejemplo a través de las tendencias de Google (Google Trends), y se ha demostrado que estos datos tienen capacidades predictivas. El objetivo principal de este proyecto es aplicar modelos de Deep Learning para pronosticar el volumen de turistas en La Costa del Sol en una época determinada del año, utilizando datos de agencias oficiales y también de las tendencias de Google. Los datos se analizaron como una serie temporal utilizando modelos univariados y multivariados utilizando los modelos de memoria a corto plazo (LSTM), un tipo de redes neuronales recurrentes que han tenido mucho éxito con los datos de series temporales. Los modelos multivariados, que contienen la información de varias búsquedas de términos en las tendencias de Google superaron a los modelos univariados. Este resultado esperado confirma el potencial de las tendencias de Google para anticipar el comportamiento de las personas, que combinado con el poder de los modelos de Deep Learning constituye un enfoque muy interesante para el pronóstico del turismo, y puede ayudar a optimizar los recursos en este importante mercado mundial, y en particular para la región de la Costa del Sol

A Multidimensionality Reduction Approach to Rainfall Prediction

The rainfall has an impact on various fields and industries, including transportation, construction, tourism, health, and wildlife preservation. Accurate rainfall prediction is essential for mitigating the negative impact of rainfall on these sectors. However, previous studies on rainfall prediction have been mainly based on datasets from North America, Europe, Australia, and Central Asia, covering different periods. This study proposes using weather datasets covering the past 5 to 10 years to capture recent patterns in weather data. Additionally, the curse of dimensionality can impact model performance and lead to overfitting. Therefore, this study proposes utilizing dimensionality reduction techniques to ensure that only the significant features are used for rainfall prediction. Multiple Linear Regression (MLR) with dimensionality reduction is applied to improve the accuracy of rainfall prediction. The experimental result shows that UMAP+MLR and t-SNE+MLR have lower MSEs of 57.27 and 56.74 and higher r2 scores of 0.130 and 0.138, respectively. The proposed approach can be valuable in optimizing resource utilization and mitigating the impacts of rainfall on various fields and industries. The source code for our research is available on GitHub repository: https://github.com/Prasanjit-Dey/Dimension_Reduction

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

Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

Neural network based country wise risk prediction of COVID-19

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlie

Dubai Taxi Demand Hotspots Prediction

Public transportation mode like taxi service consider as an essential service in every city that can serve all gender, age, and level of people, used for to move at any time and everywhere inside or outside the city. with the new entrance of technology for transportation, the taxi industry is undergoing a rapid digital transition like many other fields, that include new inventory like Uber and Careem for taxi sharing used by smartphones. Most of the time, taxi vehicles\u27 distribution is imbalanced due to passengers and taxi drivers\u27 unorganized demand. The plan is always left to the driver to estimate the right place to drive in, making passengers waiting time is longer in some areas, and taxi drivers tour without giving exemplary service. That will lead to loss of income for taxi service providers and reduce the service\u27s passenger satisfaction due to long waiting time without finding the service when needed. To solve this problem, the ability to forecast the proper place and time for taxi demand will help in solving this issue and increase income and customer satisfaction. Solving this issue will bring advantages for passengers, taxi drivers, and the service provider. Such service providers like Dubai RTA or Uber can reallocate taxi vehicles in advance to service a wider area of demand. Of course, we are not able to know where the passenger will be in a short time. However, through experience, we will know the approximate numbers of people in a particular area that require a certain number of taxis, and this is what we are looking for to reduce the waiting time. This issue is considered the right question for an approach for competitive study and using different algorithms. Can it provide the service provider with a good view of the number of riders waiting for the taxi vehicle? Moreover, a clear idea of locating the vehicles based on passengers waiting for the service. Passenger demands can also have too irregular patterns for people to understand but can be identified by a competitive study