## 4 Discussion and Limitations
### Discussion of Specific Topics
(2018), and solar energy production [PERSON] et al. (2018). 2.2 Implications of Time Series Forecasting and Artificial Intelligence for Tourism and Economic Activities  
The impact of time series forecasting and artificial intelligence on economic activities will be discussed further, investigating five relevant papers. An evaluation of portfolio management using time series and AI was accomplished by [PERSON] et al. [PERSON] et al. (2018), who also took inflation into account. The scope of the research was focused on using AI to predict the inflation rate and stock index evolution and to define a portfolio of indexes that would protect investments against inflation. Two algorithms were tested: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid model that contained both algorithms for inflation forecasting. The outcome showed that errors in inflation rate forecasting account for a quarter of stock market index estimation, recommending investments in the national economy for economic rebalancing. A forecast for sustainable tourism in Saudi Arabia was performed by [PERSON] et al. [PERSON] et al. (2018), who investigated the behavior of tourists during the COVID-19 pandemic. The dataset was obtained from the Saudi Tourism Authority and contained information from 2015 through 2021. Multiple AI algorithms, such as K-Neighbors Classifiers, Gaussian Naive Bayes,Support Vector Classifiers, Decision Trees, ARIMA, and Random Forests, were tested. The results portrayed a positive trend in the number of tourists, providing insights regarding future decisions and plans for sustainable development of the tourism sector. The mean expenditure of tourists was estimated as SAR 89,443 per trip, with a value of SAR 9198 per night, according to the statistical tests that were performed. The LSTM algorithm was implemented by [PERSON] et al. [91] for time series forecasting in the tourism sector, focusing on daily consumer and tourist datasets from Jiazhaigou, a popular tourist attraction in China, containing information for the period from 8 October 2013 to 7 August 2017. A prediction for the next 150 days was performed, obtaining good results according to statistical metrics that had been applied for training and testing, with the authors confirming that LSTM stands as one of the most suitable models for tourism prediction. Supply chain management is one of the main elements of the economic sector in each country, and it is crucial in predicting future sales with high accuracy. [PERSON] et al. [92] proposed a framework using Genetic Programming, an AI model that estimates export sales for the next 6 weeks. A causal estimation was included, facilitating extraction of the behavior between features, together with the previous trends and behavior. A variable sensitivity analysis was performed for the algorithm, and the results were compared with real sales data. Economic recession was the focus of [PERSON] et al. [93], who also included share prices in the logistics sector for the purpose of improving decision-making and estimating future share prices in the logistics industry. The LSTM algorithm was tested on historical data from five logistics companies in Hong Kong. The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were the metrics used, demonstrating that LSTM can efficiently predict future share prices. The LSTM model was trained with multiple hyperparameters, and the optimum algorithm obtained an RMSE value of 0.43%. Economic recession forecasts were performed using the stock predictions and LSTM model. Based on the scientific approaches discussed above, time series forecasting and AI have significant implications for economic activities, and their use is being sought more and more by stakeholders for predicting the number of tourists [90; 91], stock price trends [89; 93], and sales trends [92].