We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| y-bar/ml-based-anomaly-detection | pwc_unofficial | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| extrapolate_next | Ran | y-bar/ml-based-anomaly-detection/sranodec/util.py code served (permissive licence) · get_code("46056af0d721270b") |
| marge_series | Ran | y-bar/ml-based-anomaly-detection/sranodec/util.py code served (permissive licence) · get_code("26ab759901f593db") |
| series_filter | Ran | y-bar/ml-based-anomaly-detection/sranodec/util.py code served (permissive licence) · get_code("c853d2ff5cb1d977") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.03821")
get_code_for_paper("1906.03821")
have("1906.03821")
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