# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 1 Introduction  
Perishability is one of the most important characteristics of the tourism industry, making the need for accurate tourism demand (TD) forecasting crucial [1]. Governments and organizations always need to accurately estimate the expected TD in order to make valid policy planning, tactical and operational decisions [2, 3]. Accurate TD forecasting can effectively boost economic development and employment [4]; hence, the need for accurate TD forecasting is widely recognized [5]. Quantitative TD forecasting techniques can be divided into time series models, econometric approach and artificial intelligence (AI) techniques [6]. However, no single technique outperforms others on all scenarios in terms of accuracy. Time series models have been very popular for TD forecasting, and their advantage lies in the validity and efficiency of autoregressive integrated moving average (ARIMA) and its variants [7]. Most ARIMA variants are subject to some limitations, such as the assumption of a linear relation between future and past time step values, and the number of observations [8]. Therefore, when solving complex nonlinear problems, the estimates obtained may be inaccurate. Econometric methods can determine the cause-and-effect relation between TD dependent variables and independent variables [9]. However, most econometric models have several limitations. For instance, the independent variables are either exogenous or endogenous, and are decided in advance before the modelling process [10]. AI techniques including machine learning and deep learning are becoming increasingly popular in TD forecasting [11, 12]. Among the AI techniques, artificial neural networks (ANN) provide a potential alternative for solving complex nonlinear problems. Numerous studies showed that ANNs generally outperformed other methods [3, 5, 13, 14, 15, 16]. In general, AI technology can approximate arbitrarily complex nonlinear dynamic systems without any initial or extra information about data such as distribution. This brings considerable benefits and simplifications to modelling, but on the other hand, AI techniques hardlyprovide any information about potential determinism or even process understanding. However, since we are interested in TD patterns rather than physical processes, this model property is not the main disadvantage. With the advancement of ANNs, researchers find that deep learning methods, especially recurrent neural network (RNN) architectures, are more suitable than feedforward neural networks in dealing with the complexity of time series [17]. However, RNN training has the problem of vanishing gradients, so various variants of RNN models have been proposed, such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The aforementioned networks are different from other methods in that they back-propagate through immediate historical data and current data, and are more suitable for detecting development trends. The architecture of these networks overcame the weaknesses of traditional RNNs in capturing long-term dependencies, as shown by [PERSON] et al. [18]. With this feature, these networks have been widely used to solve time series forecasting problems [11; 19; 20; 21; 22; 23; 24; 25]. According to Annual Survey Report on Visitors Expenditure and Trends in Taiwan, the total revenue of international tourism increased from US$5936 million in 2008 to US$14,411 million in 2019. In recent years, international tourism has become a key service industry in Taiwan. Since 2008, Taiwan's international tourism revenue has exceeded domestic tourism revenue. If this momentum can be maintained, it will contribute to the development of the tourism industry and economic growth in the future. The outstanding application results of LSTM networks and its variants in different fields show that they can not only seize the changing data trend, but also describe the dependence of time series data. Therefore, this research tries to use an LSTM network and its variants to predict Taiwan's TD.