基于时间序列高效卷积神经网络的农机备件需求预测方法

Method for Agricultural Machinery Spare Parts Demand Forecasting Based on Time Series Efficient Convolutional Neural Networks

摘要:

农机备件是农机维修的重要基础，是农机故障及时维修和农业生产正常开展的必要保障，因此，对农机备件需求量的精准预测至关重要。然而，农机备件的需求量具有非平稳性、非线性、多零值、波动大等特点，使得预测任务变得困难。本文提出了一种基于卷积神经网络的时间序列高效卷积网络(Time series efficient convolution network, TECNet)，用于农机备件需求量的预测。该模型首先利用快速傅里叶变换对原始一维序列进行周期性提取，然后根据周期性构建二维时间序列卷积模块进行特征提取，最后将二维特征重塑回一维特征，并通过线性变换得到预测值。利用某农机备件供应商4种不同备件类型的销售数据进行了评估验证，并引入均方根缩放误差作为衡量指标，以统一不同序列间的预测效果。试验结果表明，提出的模型预测效果显著优于其他参考模型，4种不同备件需求量预测的均方根缩放误差分别为0.775、1.349、0.822、0.205，均表现出良好的预测效果。该模型能有效考虑时间序列中的时间依赖关系，具有捕捉时间序列数据中非线性模式的能力，对不同农机备件类型的预测任务均能取得良好的效果，可为预测农机备件需求量提供参考。

Abstract:

Agricultural machinery spare parts are the foundation for the repair of agricultural machinery and are essential for timely maintenance of machinery failures and the normal operation of agricultural production. Therefore, accurate forecasting of the demand for agricultural machinery spare parts is crucial. However, the demand for agricultural machinery spare parts is characterized by non-stationarity, non-linearity, multiple zero values, and large fluctuations, making the prediction task challenging. A time-series efficient convolution network (TECNet) was proposed based on convolutional neural networks for predicting the demand of agricultural machinery spare parts. The model firstly extracted the periodicity of the original one-dimensional sequence by using fast Fourier transform, then a two-dimensional time series convolution module for feature extraction was constructed based on the periodicity, and finally the two-dimensional features back was reshaped to one-dimensional features and the predicted values were obtained by linear transformation. The sales data of four different spare part types from an agricultural machinery spare part supplier were used to evaluate and validate the model, and the root mean square scaling error was introduced as a measure to unify the prediction effect among different sequences. The findings from the experiment indicated that the predictive performance of the new model surpassed that of other comparative models. The root mean square errors for the projections of the four distinct spare parts-demand were 0.775, 1.349, 0.822, and 0.205, respectively, demonstrating a high degree of accuracy in predicting. The model was capable of analyzing the time-dependent relationships within time series data, effectively identifying nonlinear patterns. It performed well in predicting the demand for various agricultural machinery spare parts, offering valuable insights for the demand of predicting agricultural machinery spare parts.