Flight delays and flight cancellations have always been a problem for the aviation industry. However, the different nature of both phenomena has made research focus almost solely on studying and predicting delays. This is due to the fact that, ultimately, it is the airline who decides whether a flight gets cancelled, whereas delays are an involuntary result of a vast array of different causes, many times due to bad management practices by airports and airlines. The literature has studied delays from a wide range of perspectives, taking into consideration several factors that influence them. Some studies have predicted delays from a machine learning perspective, while others have taken into consideration the importance of the time series component of the data. However, research shows that it is actually flight cancellations that is the most important determinant for consumer dissatisfaction and complaints, being detrimental for airlines' reputation and resulting in passengers switching carriers. Therefore, a more careful study and comprehension of what drives and affects flight cancellations is needed. Analyzing the research that has focused on understanding the underlying patterns of cancellations, what can mostly be found are theoretical and machine learning approaches. Some findings have been made in determining what further increases or helps reduce the number of cancellations, like the importance of a well-managed airport capacity to improve service quality in terms of cancellations \citep{mead2000flight}. As mentioned, there is also behavioral research on the consequences that cancellations have on airlines (Yanying et al., 2019), pointing towards an increased dissatisfaction and distrust from customers, resulting in serious damages for the airline's corporate reputation and passengers' loyalty. Nevertheless, there are components of the understanding of cancellations that remained unclear. On the one hand, a thorough time series analysis of cancellations needs to be done. In fact, as Lemke et al. (p. 85, 2009) point out, the diverse characteristics and underlying data generation processes of time series has resulted in the fact that "it seems as if no method has ever proven successful across various studies and time series". On the other hand, delays and cancellations are two phenomena that cannot be completely understood independently and, although there is a vast number of studies analyzing delay propagation, there are no conclusive results on the impact of delays on cancellations. Therefore, research must determine whether taking delays into account when analyzing cancellations improves the accuracy of cancellations forecasts and the relation among these parameters. Lastly, as they cannot only be studied alone, a more thorough study of the capacity factors that influence the number of cancellations also needs to be done. Moreover, the outbreak of the COVID-19 in the midst of the research process made the accuracy of the forecasts deviate. Delays and cancellations have evolved dramatically differently over the first months of 2020. Hence, there is a need for taking a new parameter into account that would help make sense of the abnormal cancellations in 2020 and improve forecasts accuracies. For this, the behavioral changes of the population have been taking into consideration, which has been done with Google Trends. Also, it opened a door for understanding the passengers' behavioral reaction towards air travel under these circumstances, taking into consideration both local and global factors. Therefore, this study is divided into three sections. The first one studies the relationship between delays and cancellations from a time series perspective, and it is found that taking delays into account as a parameter in the study of cancellations improves the accuracy of time series forecasts at different levels of aggregation. The second one focuses on studying the relevance of competition and network factors in the distribution of cancellations. Flights arriving or departing from a hub airport are found to be less likely to be cancelled, pointing towards the relevance of maintaining networks for airlines, thus strengthening passenger reliability and trust. However, it was found that route and airport competition, while confirming the nature of the impact, was not statistically significant in predicting flight cancellations. Finally, it was found that public concern in the context of a global pandemic varies according to local circumstances, and that shortly after the first and most shocking news, both concern and a positive consumer attitude decrease to a stabilized level, which indicating double-edged passive behavior, in which both concern and willingness to purchase flight or event tickets (i.e., requiring travel or social gatherings) are reduced to similarly low levels for at least one month after the initial mayhe
