title: ÐÐµÑÐ¾Ð´ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ Transformer
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Research on anomaly detection method for time series based on transformer architecture
Creators: name: ÐÐ°Ð½ Ð¡ÑÑÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐµÑÐ²ÐµÑÐ½ÑÐµ Ð¼ÐµÑÑÐ¸ÐºÐ¸
Ð°Ð½Ð°Ð»Ð¸Ð· Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÐ°
server metrics
time series analysis
anomaly detection
architecture

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐµÑÐ¾Ð´ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ TransformerÂ». Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð´Ð²ÑÑÑÑÐ°Ð¿Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð¾ÑÑÐ°ÑÐºÐ¾Ð², Ð¿ÑÐµÐ´Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð½Ð°Ñ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð² ÐºÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¸Ð»Ð¸ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÑÐµÑÐ²ÐµÑÐ¾Ð². ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð½Ð°Ñ ÑÐ°ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Â«ÐÐµÐ·Ð°Ð²Ð¸ÑÐ¸Ð¼ÑÐ¹ Ð¾Ñ ÐºÐ°Ð½Ð°Ð»Ð¾Ð² ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼ÐµÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ñ Ð¿Ð°ÑÑÐ°Ð¼Ð¸ (PatchTST)Â». ÐÐ»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¾ÑÑÐ°ÑÐºÐ¾Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿Ð¾ÑÐ¾Ð³Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐºÐ¾Ð»ÑÐ·ÑÑÐµÐ³Ð¾ Ð¾ÐºÐ½Ð°. Ð§ÑÐ¾Ð±Ñ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾ Ð²ÑÑÐ²Ð»ÑÑÑ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¸ Ð² Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÑ ÑÐµÑÐ²ÐµÑÐ¾Ð², Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¾ÑÑÐ°ÑÐºÐ¾Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð¼ÐµÑÐ¾Ð´ ÑÐºÑÐ¿Ð¾Ð½ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²Ð·Ð²ÐµÑÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐºÐ¾Ð»ÑÐ·ÑÑÐµÐ³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ (EWMA) Ð´Ð»Ñ ÑÐ³Ð»Ð°Ð¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð¾ÑÑÐ°ÑÐºÐ¾Ð² Ð¸ Ð¿Ð¾ÑÐ¾Ð³Ð¾Ð². ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±ÑÐµÐ·ÐºÐ¸ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð´Ð»Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ð¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð»Ð¾Ð¶Ð½ÑÑ ÑÑÐ°Ð±Ð°ÑÑÐ²Ð°Ð½Ð¸Ð¹, ÑÑÐ¾ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¿Ð¾Ð²ÑÑÐ°ÐµÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð­ÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐ°Ñ AIOps-2018-KPI Ð¸ (Ð¡ÐÐ¦) Ð¡ÐÐ±ÐÐ£. Ð­ÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐµ AIOps-2018-KPI. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÑÐµÑ ÑÐ²Ð½ÑÐµ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð° Ð¿Ð¾ Ð²ÑÐµÐ¼ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ¼.
description: The topic of the graduate qualification work is Â«Research on anomaly detection method for time series based on transformer architectureÂ». This thesis proposes a two-stage model for detecting anomalies in time series using predictive residuals, aimed at identifying anomalies in critical metrics of server software or hardware. The predictive component of the model employs the "Channel-Independent Time Series Transformer with Patches (PatchTST)." For residual analysis, a dynamic threshold algorithm based on a sliding window is used. To enhance the accuracy of anomaly detection in server metrics, the residual analysis model utilizes the exponentially weighted moving average (EWMA) method for smoothing residuals and thresholds. An anomaly trimming algorithm is applied to filter out potential false positives, significantly improving the models accuracy. The effectiveness of the predictive model was validated on the AIOps-2018-KPI and (SCC) SPbPU datasets, while the anomaly detection models effectiveness was tested on the AIOps-2018-KPI dataset. Experimental results indicate that the proposed method exhibits clear advantages across all metrics.