# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 1 Introduction  
Computer operation and maintenance is always a vital component in guaranteeing the high availability of the application systems. Operation and maintenance must evolve from manual detection to intelligent detection with the explosive increase in the volume of application data. According to Gartner's report, more than 40% of global enterprises have replaced their outdated O&M systems with intelligent solutions as of 2020. In these intelligent systems, anomaly detection is critical to detect important performance indicators (KPIs) such as CPU utilization, memory utilization and so on. To ensure a stable and reliable O&M system, a rising number of researchers are investigating KPI anomaly detection methods [1, 2]. Traditional statistics, supervised learning and unsupervised learning algorithms are the three types of KPI anomaly detection techniques. First, seasonal length is required as an input parameter by traditional statistical approaches such as Argus [3] and TSD [4], but it is frequently given manually. It may cause seasonality to be disrupted in intricate KPI profiles, leading to erroneous anomaly detection. Secondly, supervised learning algorithms such as Opperence [5] and EGADS [6] relied on classical statistical techniques, and they also did not recognize seasonal length under intricate KPI profiles. Finally, among unsupervised learning methods, [PERSON] [7] developed a periodic adjustable approach called Period. This paper considers time series data to be related to daily human activities, and it directly assumed that the basic seasonal length of time-series data is 1 day. However, KPI timeseries data containing intricate KPI profiles are very common, the seasonal length in these non-Gaussian distributed data is difficult to estimate [8]. In general, there are three key challenges to overcome. To begin, precise seasonal characteristics are hard to extract from the intricate KPI time series data. Second, due to the long sub-sequence length, the clustering process will take too much time. Third, noise and anomalies in the KPI time series data could also result in bad sub-sequence clustering results. Facing the above problems, existing KPI anomaly detection algorithms cannot obtain good performance under intricate KPI profiles. To address the aforementioned issues, this work introduces a seasonal adaptive KPI anomaly detection algorithm \(ASAD\) to enhance the detection accuracy under intricate KPI profiles. For the first challenge, we adopt the scaling technique (enlarge-detect-restore) to determine the seasonal length under intricate KPI profiles. For the second challenge, to reduce the time consumption, we develop a new clustering algorithm by extracting the principal information rather than using the raw data. For the third challenge, we introduce the calendar feature to further modify the clustering results, avoiding noise and anomalies. At last, according to our experiments, \(ASAD\) can recognize the seasonal length of time series data under intricate KPI profiles and effectively boosting anomaly detection accuracy. The contributions of our study are summarized as follows. * We present a scaling Auto-Period approach using the philosophy of enlarge-detect-restore, to determine the seasonal length under intricate KPI profiles. * This study develops a new eBeats clustering algorithm, which reduce the large time overhead of KPI sub-sequence clustering process. eBeats first extracts the principal information based on discrete cosine transform, then clusters the principal information. * The calendar-based correction technique is introduced to improve clustering results with noise and anomalies. It could improve clustering results by using the relationship between seasonality and calendar, which not only improves accuracy but also provides great robustness. The remainder of the paper is laid out as follows. Section 2 introduces some concepts and related studies. The framework of the \(ASAD\) algorithm is described in Section 3, as well as the algorithm's premise.