This is a python implementation of Twitter's AnomalyDetection and BreakoutDetection.

The dependencies contain C++ and Fortran code, so that you need gcc installed. Checkout the code, enter the folder and run:

pip install -r requirements.txt

When use this as a library, please include the line for "pyloess" from "requirements.txt" in your "requirements.txt".

The parameters are the same as the AnomalyDetectionVec in Twitter's AnomalyDetection (except the plot related ones). You need to put your time series data into a list of float numbers:

from anoms import detect_anoms
from breakout import detect_breakout

x = list()

\# put the data into x

res = detect_anoms(x, max_anoms=0.02, alpha=0.01, direction='both')

res will be a list of int numbers, consists the index of detected anomalies in x. If e_value=True is set, res will be a tuple, whose first value is the list of index of detected anomalies and the second value is the list of expected values.

res = detect_breakout(x, min_size=24, method='multi', beta=0.001, degree=1)

res will be a list of int numbers, consists the index of detected breakout in x.