Application of time series decomposition for forecasting risk dynamics while ensuring safety of flights
In this paper, the methods of time series decomposition, their application to the decomposition of risk dynamics, as well as forecasting the dynamics of risks taking into account the decomposition were considered. The classical decomposition, additive and multiplicative types are described, the additive decomposition algorithm is considered. The moving average method and the weighted moving average are considered. The dynamics of risks is considered as a time series. As a decomposition method, the STL method was chosen as a sufficiently flexible and emission-resistant (robust) decomposition method, in addition, the STL method has a number of significant advantages over classical decomposition. The application of this decomposition method to the dynamics of risks allows us to study the components of the time series, in particular, it can be noted that the spread of the random component increases over time, this can adversely affect the accuracy of forecasting the dynamics of risks. A seasonal naive method is used to predict the seasonal component. For forecasting the seasonally adjusted components may be any non-seasonal method, for example, the method of random walk with drift. Prediction of risk dynamics using STL decomposition and random walk method showed that the average absolute percentage error (MAPE) in this prediction was 4.6% .