MSTREAM
MSTREAM 1 can detect unusual group anomalies as they occur, in a dynamic manner. MSTREAM has the following properties:
- (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes;
- (b) it is online, thus processing each record in constant time and constant memory;
- (c) it can capture the correlation between multiple aspects of the data.
Parameters
-
num_rows(
int, Default:2) → Number of Hash Functions. -
num_buckets(
int, Default:factor) → Number of Buckets for hashing. -
factor(
float, Default:0.8) → Temporal Decay Factor.
Example Usage
We can create an instance and deploy LBC model like this.
import turboml as tb
model = tb.MStream()Footnotes
-
Bhatia, S., Jain, A., Li, P., Kumar, R., & Hooi, B. (2021, April). Mstream: Fast anomaly detection in multi-aspect streams. In Proceedings of the Web Conference 2021 (pp. 3371-3382). ↩