From 960d784cf19ea5520a596de774678691dd8b29b6 Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Mon, 18 Nov 2024 09:24:47 +0800 Subject: [PATCH] refactor: update docs. --- .../02-anomaly-detection.md | 23 ------------------- 1 file changed, 23 deletions(-) delete mode 100644 docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md 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 deleted file mode 100644 index b7da6ef627..0000000000 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-anomaly-detection.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -title: "检测算法" -sidebar_label: "检测算法" ---- - -本节介绍内置异常检测算法模型的定义和使用方法。 - -## 概述 -分析平台内置了6个异常检查模型，分为3个类别，分别是基于统计学的模型、基于数据密度的模型、以及基于深度学习的模型。在不指定异常检测使用的方法的情况下，默认调用 iqr 进行异常检测。 - - - - - - - -### 参考文献 -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). -5. Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4. -