Yield forecast of California strawberry: Time-series Models vs. ML Tools

摘要

In this study, a comparison of time-series modeling with linear and nonlinear ML tools is conducted for fresh produce (FP) yield forecast. The consecutive monthly weather and yield dataset of Oxnard, California, corresponding to the years 2007 to 2014, are applied for models’ development and training by examining the diverse combinations of predictors. The forecast performance is then assessed on the next two years ahead. The sensitivity analysis is performed as the preprocessing approach to ascertain the effective lag-time of the predictors. Results reveal the efficiency of time-series analysis and modeling in FP yield forecast as the implementation of autoregressive predictors along with the exogenous variables, significantly improves the forecast accuracy.