Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ADSaS: Comprehensive Real-time Anomaly Detection System   Since with massive data growth, the need for autonomous and generic anomaly
detection system is increased. However, developing one stand-alone generic
anomaly detection system that is accurate and fast is still a challenge. In
this paper, we propose conventional time-series analysis approaches, the
Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal
Trend decomposition using Loess (STL), to detect complex and various anomalies.
Usually, SARIMA and STL are used only for stationary and periodic time-series,
but by combining, we show they can detect anomalies with high accuracy for data
that is even noisy and non-periodic. We compared the algorithm to Long Short
Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection
system. We used a total of seven real-world datasets and four artificial
datasets with different time-series properties to verify the performance of the
proposed algorithm.

15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom