Comments (2)
同学你好。
MAE 15.48, MAPE 10.34 RMSE 24.96(PDFormer)这个是12步平均的结果,而且是6:2:2的数据划分。这个是遵循过去文章的规范,对于这个数据集都是6:2:2的划分方案。
MAE 16.10, MAPE 11.28 RMSE 24.70(Unified Data Management and Comprehensive Performance
Evaluation for Urban Spatial-Temporal Prediction) 这个是12步平均的结果,而且是7:1:2的数据划分。这个是因为这个文章比较了很多数据,采用的通义的7:1:2, 是DCRNN论文开创的划分方案。
最后,MAE 16.98, MAPE 12.87 RMSE 27.75(Towards Efficient and Comprehensive Urban Spatial-Temporal
Prediction: A Unified Library and Performance Benchmark) 这个首先例子不太对,因为这个文章提供的不是12步平均,因为arxiv文章的空间不受限制,所以给的第3,6,12步的结果,对于多步预测,第12步的结果肯定比12步平均差的呀,所以这个结果比较大。
如果不太理解平均和单步,可以看这个:#359
最后,也是为了避免误解,Towards Efficient and Comprehensive Urban Spatial-Temporal
Prediction: A Unified Library and Performance Benchmark这个arxiv目前改成LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction了,作为libcity短文的扩展版本,不再放实验结果了。
from bigscity-libcity.
好的,了解了,非常感谢
from bigscity-libcity.
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from bigscity-libcity.