Biased resampling strategies for imbalanced spatio-temporal forecasting
Biased resampling strategies for imbalanced spatio-temporal forecasting
复制标题
时空不平衡预测的偏差重采样策略
DOI：
10.1007/s41060-021-00256-2
复制
发表时间：
2019
影响因子：
2.4
通讯作者：
V. S. Costa
中科院分区：
文献类型：
--
作者：
Mariana Oliveira;Nuno Moniz;Luís Torgo;V. S. Costa
极端和罕见的事件，如空气污染高峰或异常天气状况，可能会产生严重影响。许多这类事件都是通过时空过程发展起来的。及时和准确的预测是应对其影响的最有价值的工具。我们提出了一套新的不平衡的时空预测任务，引入偏见到以前的随机过程的resception策略。该偏差是空间和时间权重的组合，其可以是静态的或相关性感知的，并且包括超参数，该超参数调节在欠采样或过采样期间的观测选择中的时间和空间维度的相对重要性。我们使用3种不同的现成学习算法，在10个不同的地理参考数字时间序列上测试和比较我们的建议与标准版本的策略。实验结果表明，我们的建议提供了一个优势，在不平衡的数值时空预测任务的随机reservation策略。
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.