Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet

Abstract

To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.