Multi-Step Time Series Forecasting for Intermittent Demand Based on Transformer

Consumable spare parts for civil aircraft are characterized by intermittent demand patterns. Constrained by long order replenishment cycles, traditional single-step demand forecasting struggles to effectively support inventory decisions. Moreover, research on intermittent demand forecasting primarily focuses on single-step, short-term cycle predictions, while multi-step forecasting using advanced deep learning methods remains in its early stages. To address this issue, this study introduces a multi-step forecasting approach based on the Transformer architecture, employing a MIMO strategy to achieve long time series forecasting for intermittent demand. The model is validated using a dataset from an airline maintenance base consisting of 360 intermittent demand items. A comparative analysis is conducted against three traditional time series models (SES, Croston, and SBA) and several neural network architectures (FNN, GRU, and LSTM). The performance of different methods was compared using six evaluation metrics. Results demonstrate that the Transformer consistently delivers outstanding performance across various forecast horizons. Notably, in long-horizon forecasting tasks, the model’s advantage in prediction accuracy becomes increasingly significant as the forecast step increases.