Dept. of Plant Science, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Dept. of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
PLoS One. 2019 Jul 25;14(7):e0219889. doi: 10.1371/journal.pone.0219889. eCollection 2019.
Demand for spring onion seeds is variable and maintaining its supply is crucial to the success of seed companies. Spring onion seed demand forecasting, which can help reduce the high operational costs increased by long-period propagation and complex logistics, has not previously been investigated yet. This paper provides a novel perspective on spring onion seed demand forecasting and proposes a hybrid Holt-Winters and support vector machine (SVM) forecasting model. The model uses dynamic factors, including historical seed sales, seed inventory, spring onion crop market price and weather data, as inputs to forecast spring onion seed demand. Forecasting error, i.e. the difference between actual and forecasted demand, is assessed. Two advanced machine learning models are trained on the same dataset as benchmark models. Numerical experiments using actual commercial sales data for three spring onion seed varieties show the proposed hybrid model outperformed the statistical-based models for all three forecasting errors. Seed inventory, spring onion crop market price and historical seed sales are the most important dynamic factors, among which seed inventory has short-term influence while other two have mid-term influence on seed demand forecasting. The absolute minimum temperature is the only factor having long-term influence. This study provides a promising spring onion seed demand forecasting model that helps understand the relationships between seed demand and other dynamic factors and the model could potentially be applied to demand forecasting of other crop seeds to reduce total operational costs.
对大葱种子的需求是多变的，维持其供应对于种子公司的成功至关重要。大葱种子需求预测可以帮助降低因长期繁殖和复杂物流而增加的高运营成本，但尚未进行过相关研究。本文为大葱种子需求预测提供了新的视角，并提出了一种混合 Holt-Winters 和支持向量机（SVM）预测模型。该模型使用动态因素，包括历史种子销售、种子库存、大葱作物市场价格和天气数据，作为预测大葱种子需求的输入。预测误差，即实际需求与预测需求之间的差异，进行了评估。两个先进的机器学习模型在相同的数据集上作为基准模型进行训练。使用三种大葱种子品种的实际商业销售数据进行的数值实验表明，所提出的混合模型在所有三种预测误差方面都优于基于统计的模型。种子库存、大葱作物市场价格和历史种子销售是最重要的动态因素，其中种子库存具有短期影响，而其他两个因素对种子需求预测具有中期影响。绝对最低温度是唯一具有长期影响的因素。本研究提供了一种有前途的大葱种子需求预测模型，有助于理解种子需求与其他动态因素之间的关系，该模型可能被应用于其他作物种子的需求预测，以降低总运营成本。
