Intermittent Time Series Demand Forecasting Using Dual Convolutional Neural Networks
Abstract
Forecasting intermittent demands is challenging due to their irregular and unpredictable demand pattern. This makes the businesses unprepared for upcoming demands, where the conventional methods often fail to predict the demand occurrence pattern sufficiently. In this paper, we proposed a two-step approach, "UR2CUTE," (Using Repetitively 2 CNN for Unsteady Timeseries Estimation), employing Convolutional Neural Networks (CNNs) specifically designed to handle the unique challenges of intermittent time series. CNNs, known for their effectiveness in capturing spatial and temporal patterns in data, offer a promising area to improve forecast accuracy in predicting time series demand patterns. Our approach presents a combined process for intermittent demand forecasting. A CNN model is initially designed as a binary classifier to determine demand occurrence. Afterward, a distinct CNN model is employed to estimate the magnitude of the demand. This dual-phase approach improves forecasting accuracy in intermittent demands, specifically in predicting the non-demand (Zero-Demand). The suggested approach notably surpasses traditional forecasting techniques, including Croston's method, which is tailored for intermittent demand forecasting. It also outperforms other methods like XGboost, Random Forest, ETR, Prophet, and AutoArima, especially in predicting the lead time demand distribution for sporadic demands. The deployment of dual CNN models facilitates a deeper understanding of intermittent demand dynamics. This, in turn, enhances supply chain management effectiveness and efficiency, offering a robust solution to the complex challenges of intermittent demand forecasting.
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