While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

中文翻译：

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预测活动的发生。

虽然活动识别已被证明对普及计算应用程序很有价值，但较​​少的工作集中在预测未来活动发生的技术上。我们提出了一种活动预测方法来预测目标活动发生之前的时间。该方法使用回归树分类器生成活动预测，并提供优于序列预测方法的优势，因为它可以预测活动发生之前的预期时间。我们在现实世界的智能家居数据集上评估该算法，并提供证据证明我们提出的方法在预测活动时间方面最有效。