| dc.description.abstract | The civil aviation transport industry plays a significant role in the globalized economic system, facilitating trade and business travel. Recently, it has faced challenges such as international conflicts, trade disputes, and the reorganization of global supply chain, leading to fluctuations in financial markets, stock and exchange rates, and energy supply and demand. Fuel expenses constitute the largest operating cost for airlines, making effective fuel price prediction crucial. This study aims to develop an aviation fuel price prediction model, using four time series forecasting methods: univariate, multivariate, multi-step, and recursive models. The research utilizes historical fuel price data combined with data from major crude oil markets, the S&P 500 index, and the US dollar index. The predictive techniques integrate the Autoregressive Integrated Moving Average model with four deep learning recurrent neural network architectures： Simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, and Bidirectional LSTM, using grid search to find the optimal hyperparameter configuration. The results indicate that the LSTM-based prediction models outperform the ARIMA model across various forecasting methods. For next-week price prediction, the GRU model achieves a Mean Absolute Percentage Error of 3.54%, while for next-month prediction, the BiLSTM model achieves a MAPE of 7.57%. The performance of multivariate prediction is slightly weaker than that of univariate prediction, due to the suboptimal feature weighting among multiple sequences. In long-term forecasting, the proposed model reduces the error in predicting the next year’s fuel prices by an average of 7.97% compared to budgeted price benchmarks, demonstrating the LSTM model′s excellent performance in short-term prediction and its potential for long-term forecasting. | en_US |