Spring onion seed demand forecasting using a hybrid Holt-Winters and support vector machine model.

ABSTRACT: 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 d

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC6658075 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature