Reliability of spare parts inventory in the company is one of the most significant challenges in the field of maintenance and repairs, but on the other hand, the liquidity crisis resulting from the purchase of surplus spare parts is another challenge facing the organizational financial field. Accordingly, accurate forecasting of future consumption is one of the most important solutions for inventory control systems. But because of the impact of so many variables on spare part consumption, most real-world data is chaotic. This leads to the use of classical methods to predict future demand, with high error and low reliability. In this research, a novel and reliable hybrid model based on the support vector machine (SVM), and two single algorithms (STL Decomposed ARIMA and three-layer feed-forward neural network) has been presented to predict the future consumption of spare parts. The proposed model (SVM-ARIMA-3LFFNN hybrid model) also experiments on several chaotic time series in the rapid miner repositories. The forecasting results indicate that the proposed hybrid model attains superior performance compared with a single model and can adapt to chaotic time series. Performance criteria considered in this study are MAE, RMSE, MAPE, and sMAPE. The results indicate that the proposed model can improve the RMSE, MAPE, and sMAPE (up to 30% improvement).

公司备件库存的可靠性是维护和维修领域面临的最大挑战之一，但另一方面，购买过剩备件导致的流动性危机是组织财务领域面临的另一个挑战。因此，准确预测未来消耗是库存控制系统最重要的解决方案之一。但由于如此多的变量对备件消耗的影响，大多数现实世界的数据都是混乱的。这导致使用经典方法来预测未来需求，具有高误差和低可靠性。在这项研究中，一种基于支持向量机（SVM）的新颖可靠的混合模型，并提出了两种单一算法（STL Decomposed ARIMA 和三层前馈神经网络）来预测备件的未来消耗。所提出的模型（SVM-ARIMA-3LFFNN 混合模型）还在快速矿工存储库中的几个混沌时间序列上进行了实验。预测结果表明，与单一模型相比，所提出的混合模型具有更好的性能，并且能够适应混沌时间序列。本研究中考虑的性能标准是 MAE、RMSE、MAPE 和 sMAPE。结果表明，所提出的模型可以提高 RMSE、MAPE 和 sMAPE（最高提高 30%）。预测结果表明，与单一模型相比，所提出的混合模型具有更好的性能，并且能够适应混沌时间序列。本研究中考虑的性能标准是 MAE、RMSE、MAPE 和 sMAPE。结果表明，所提出的模型可以提高 RMSE、MAPE 和 sMAPE（最高提高 30%）。预测结果表明，与单一模型相比，所提出的混合模型具有更好的性能，并且能够适应混沌时间序列。本研究中考虑的性能标准是 MAE、RMSE、MAPE 和 sMAPE。结果表明，所提出的模型可以提高 RMSE、MAPE 和 sMAPE（最高提高 30%）。