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View Code? Open in Web Editor NEWA robust and noisy-resilient anomaly detection method by explicitly learning the representations of time-invariant and time-varying characteristics of multivariate KPIs
A robust and noisy-resilient anomaly detection method by explicitly learning the representations of time-invariant and time-varying characteristics of multivariate KPIs
Dear authors, how can we use this algorithm on data with other #KPIs (also named as n in the code)?Now this code will raise ValueError('Invalid Kpi numbers: {}, choose from the candidate set [16,24,38,48].'.format(self.n)) when it is tested on other datasets with different #KPI (such as 49, 19 ...). Is the algorithm only applicable on the dataset used in the paper?
Hi, I have great interest in your paper and I got a question when I running your code. What's the meaning of the code on lines of 65~66 in the evaluation.py.
**evaluation.py**
65 if current_timestamp >= start_dt and current_timestamp <= end_dt:
66 detected_anomaly_intervals_tmp[k] = ground_truth_anomaly_intervals[j]
Does this mean that the whole anomaly intervals are detected when you only find an anomaly point in an interval?
请问您在SMD这个数据集上进行的实验训练集是否为OmniAnomaly开放的SMD训练集呢?
请问您是否可以告知在该实验上您所设置的相关参数呢?我采用了您的默认参数,数据预处理后数据增强了很多倍,以我的实验设备(满足您论文中提出的实验设备能力)报错内存不够,所以想询问一下您所使用的实验参数。
感谢!
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