Component models of degradation assessment for recovery of aviation equipment during its maintenance

The main neural network architectures applied for product demand forecasting are examined. Particular attention is given to Recurrent Neural Networks (RNN), which demonstrate high efficiency when working with time series and detecting dependencies within data. A detailed analysis is provided for the Long Short-Term Memory (LSTM) architecture, an advanced variant of RNN that effectively addresses the vanishing gradient problem, a common issue in traditional RNN. Due to its capability to retain information over extended periods, LSTM is ideal for tasks requiring the analysis of long-term dependencies, such as seasonal fluctuations in product demand. The application of Convolutional Neural Networks (CNN) is analyzed, which show strong performance when processing structured data, such as images or matrices. With their ability to effectively detect spatial dependencies, CNN can be useful for demand forecasting when complex relationships between various data features need to be considered, particularly for demand evaluations dependent on multiple factors. The architectures Feedforward Neural Networks (FNN), Gated Recurrent Units (GRU), Attention-based models (ABM) and Autoencoders (AE) are considered, which can also be applied to solve demand forecasting problems. A comparative analysis of the architectures is made in terms of forecast accuracy and ability to work with time series. By evaluating the training speed, data processing time, and reliability of these architectures in terms of the stability of results and forecast reliability, it has been determined that LSTM architectures are the most effective for product demand forecasting. These networks provide high accuracy and reliability of results and perform well with time series, which is crucial for forecasting demand under dynamic conditions.