论文标题
预感网络，一种多吉米线变压器网络架构，用于草莓桌面预测
Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting
论文作者
论文摘要
产量预测是产量优化所必需的关键第一步，对更广泛的食品供应链，采购，价格谈判，物流和供应产生了重要影响。然而，众所周知，收益率预测是困难的，并且经常不准确。预感网是一种多吉米线，时间序列摄入的方法来处理过去，现在和未来的预兆。我们展示了这种结构与变压器的结合如何获得提高粮食安全，降低价格和减少浪费的关键收益率预测。我们发现数据可用性是一个持续的困难，但是使用我们的预感网络和我们自己的收集数据，我们在最新季节中，测试集RMSE损失约为0.08。
Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply. However yield forecasting is notoriously difficult, and oft-inaccurate. Premonition Net is a multi-timeline, time sequence ingesting approach towards processing the past, the present, and premonitions of the future. We show how this structure combined with transformers attains critical yield forecasting proficiency towards improving food security, lowering prices, and reducing waste. We find data availability to be a continued difficulty however using our premonition network and our own collected data we attain yield forecasts 3 weeks ahead with a a testing set RMSE loss of ~0.08 across our latest season.
