An integrated approach combining neural networks and genetic algorithms for multisource time series forecasting

Abstract

This paper proposes a data-driven framework that leverages multisource information to significantly enhance time series forecasting within the tourism domain. Specifically, we combine the Multilayer Perceptron (MLP) neural network with the NSGA-II genetic algorithm for efficient hyperparameter optimization. In addition to historical data on monthly international tourist arrivals to Vietnam, two exogenous data sources are incorporated: (i) sentiment indices extracted from tourist reviews on TripAdvisor, and (ii) search trend data from Google Trends. Critical hyperparameters, including input window size, learning rate, number of epochs, early stopping, and normalization methods, are simultaneously optimized using the NSGA-II genetic algorithm. Experimental results demonstrate that the NSGA-II-MLP model, utilizing multisource data, consistently outperforms the standard MLP and exhibits robust performance across both the pre-COVID-19 and volatile pandemic periods. The results underscore the effectiveness of combining multisource data with NSGA-II optimization for tourism demand forecasting.