From 183c1ee0caad22e7dab91c8999957891930602b8 Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Tue, 12 Nov 2024 19:10:01 +0800 Subject: [PATCH] Update 02-anomaly-detection.md --- .../06-TDgpt/05-anomaly-detection/02-anomaly-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md index 1eae6088ce..2fc26d57c1 100644 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md +++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md @@ -38,7 +38,7 @@ LOF[5]: 局部离群因子（LOF，又叫局部异常因子）算法 ### 参考文献 -1. https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule +1. [https://en.wikipedia.org/wiki/68–95–99.7 rule](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule) 2. https://en.wikipedia.org/wiki/Interquartile_range 3. Adikaram, K. K. L. B.; Hussein, M. A.; Effenberger, M.; Becker, T. (2015-01-14). "Data Transformation Technique to Improve the Outlier Detection Power of Grubbs's Test for Data Expected to Follow Linear Relation". Journal of Applied Mathematics. 2015: 1–9. doi:10.1155/2015/708948. 4. Hochenbaum, O. S. Vallis, and A. Kejariwal. 2017. Automatic Anomaly Detection in the Cloud Via Statistical Learning. arXiv preprint arXiv:1704.07706 (2017).