Twitter's Anomaly Detection is easy to use, but it's a R library.
Although there are some repos for python to run twitter's anomaly detection algorithm, but those libraies requires R installed.
This repo aims for rewriting twitter's Anomaly Detection algorithms in Python, and providing same functions for user.
pip3 install tad
1.The data should have the Index which is a datetime type. Single series is processed so only pass single numeric series at a time. 2.Plotting function is based on matplotlib, the plot is retured in the results if user wants to change any appearnaces etc.
import tad
import pandas as pd
import matplotlib.pyplot as plt
a = pd.DataFrame({'numeric_data_col1':
[1,1, 1, 1, 1, 1, 10, 1, 1, 1, 1, 1, 1, 1]},
index=pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-03',
'2020-01-04', '2020-01-05','2020-01-06','2020-01-07','2020-01-08',
'2020-01-09','2020-01-10','2020-01-11','2020-01-12','2020-01-13',
'2020-01-14']))
results = anomaly_detect_ts(a['numeric_data_col1'],
direction='both', alpha=0.02,
max_anoms=0.20,
plot=True, longterm=True)
if results['plot']: #some anoms were detected and plot was also True.
plt.show()
results {'anoms': 2020-01-14 1 2020-01-07 10 dtype: int64, 'expected': None, 'plot': <matplotlib.axes._subplots.AxesSubplot at 0x29b827b2748>}
Output shall be in the results dict
results.anoms shall contain the anomalies detected
results.plot shall contain a matplotlib plot if anoms were detected and plot was True
results.expected tries to return expected values for certain dates. TODO: inconsistent as provides different outputs compared to anoms
