两篇新研究论文探讨了用于不同领域的先进时间序列预测方法。一篇论文介绍了一种基于事件的方法，利用环境数据预测葡萄园病害风险，并比较了XGBoost和LSTM等机器学习模型。另一篇论文提出了一个具有Boosting机制的贝叶斯框架来预测肿瘤学需求趋势，在真实世界数据上表现优于ARIMA和LSTM等传统方法。 AI
影响 这些论文强调了将机器学习应用于专业时间序列预测任务的进展，有望提高农业和医疗保健领域的准确性。
排序理由 arXiv上发表的两篇不同的学术论文，详细介绍了用于时间序列预测的新机器学习方法。
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arXiv:2605.05270v1 Announce Type: cross Abstract: Accurate trend forecasting in healthcare time series is essential for planning and resource allocation. This paper proposes a Bayesian framework for predicting oncology demand trends, modeling weekly appointments as a Poisson proc…
arXiv:2605.04548v1 Announce Type: new Abstract: Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protection. However, many existing studies formulate disease prediction as daily presence…
Accurate trend forecasting in healthcare time series is essential for planning and resource allocation. This paper proposes a Bayesian framework for predicting oncology demand trends, modeling weekly appointments as a Poisson process with a Gamma prior to the demand rate. To enha…