# Optimizing Flight Delay Predictions with Scorecard Systems
## 2 Literature Review
### Conventional Forecasting Methods  
The forecasting of delays in air transportation is a critical area of research, particularly as it impacts operational efficiency, passenger satisfaction, and overall airline profitability. Traditional forecasting methods have evolved significantly, integrating various statistical methods. One of the foundational methods in delay forecasting is the autoregressive integrated moving average (ARIMA) model, which has been widely used for time series analysis in various contexts, including air traffic forecasting [15; 16; 17]. For instance, studies have shown that ARIMA models can effectively capture trends and seasonality in air passenger traffic data, making them suitable for predicting future volumes and potential delays [18; 19]. The integration of external variables, such as GDP and oil prices, into ARIMAX models has also been demonstrated to improve the accuracy of forecasting, particularly in the face of economic shocks such as the COVID-19 pandemic [20]. Although ARIMA and its variants are effective in modeling seasonality and long-term trends, they often assume linear relationships and stationary time series, which may not reflect the dynamic and irregular nature of flight delays. These models also struggle to incorporate categorical or non-numeric factors, such as airport congestion levels or flight-specific operational variables. This limits their practical usefulness in complex operational environments where nonlinear, multidimensional influences play a major role. In addition to ARIMA, hybrid models that combine traditional statistical methods with machine learning techniques have gained traction. For example, the use of ensemble methods, which aggregate predictions from multiple models, has been shown to enhance the robustness of forecasts by capturing complex patterns in the data [21]. These methods are particularly beneficial in the context of air traffic, where the interplay of various factors can lead to significant variability in delays. The application of machine learning algorithms,such as support vector machines and neural networks, has also been explored, providing a means to model non-linear relationships and improve prediction accuracy [22; 23]. However, although ensemble and hybrid models may increase predictive power, they often lack transparency and interpretability, making them difficult to deploy in environments where quick and explainable decisions are required. In addition, their computational complexity may hinder their use in real-time systems, particularly for airlines with limited technological infrastructure or data science expertise. Additionally, the impact of external factors, such as weather conditions and operational disruptions, on air traffic delays has been a focal point in recent research. Studies have indicated that incorporating weather-related variables into forecasting models can significantly enhance their predictive capabilities [24]. For example, the use of weather impact indices in conjunction with traffic volume data has been shown to produce more accurate delay predictions, particularly for short-term forecasts [25]. The COVID-19 pandemic has further complicated the forecast landscape, introducing unprecedented volatility in air travel patterns. Researchers have adapted traditional and machine learning methods to account for these disruptions, highlighting the importance of flexibility in forecasting approaches [26]. The disorder introduced by the pandemic necessitated a reassessment of pre-existing models, prompting the investigation of novel methodologies that can more effectively accommodate abrupt modifications in air traffic dynamics [27]. These shocks have highlighted the fragility of overly complex or rigid forecasting systems, reinforcing the need for adaptive yet interpretable solutions that can be updated or recalibrated quickly in response to sudden changes.