osESD 1.1.1

Anomaly detection algorithm for time series datasets

Automated Online Sequential ESD (Python)

This package includes Python codes for online sequential ESD(osESD), a variation of GESD tests. It is a accurate and efficient way to detect anomalies in an univariate time series dataset.

We provide the basic function osESD and an automated grid search method auto-osESD. auto-osESD can be used to find the best parameters for a specific dataset,
using basic parameters or parameters provided explicitly by the user.

Installation

1. Download library.

Download osESD library using pip.

pip install osESD

2. Import function.

Import function in python code. osESD is for a single online sequential ESD test, and will return indices of the anomalies. Also, it will produce a text file with these anomalies in the designated output file (default 'osESD_results'). auto_osESD is for convenient tuning of osESD, and will run with default parameters if not specified otherwise.

from osESD import osESD, auto_osESD

3. Run on dataset.

Read data and run it using osESD. The model is designed to run on univarites time series dataset in a csv. fashion. Imported csv. dataset and custom name of the dataset should be entered. Also, the name of the column with values should be entered to 'value_name' and the name of the column with anomaly values should be entered as 'anomaly_name'.

import os
import pandas as pd

from osESD import osESD, auto_osESD

os.chdir(os.path.dirname(os.path.abspath(__file__)))

df = pd.read_csv("ARIMA1_ber_1.csv")
anoms_1 = osESD(df,"A2_benchmark", value_name='value',anomaly_name='anomaly')
print(anoms_1)

anoms_2 = osESD(df,"A2_benchmark", value_name='value',anomaly_name='anomaly',size=100, dwin=5, rwin=5, maxr=6, alpha=0.01)
print(anoms_2)

auto_anoms_1 = auto_osESD(df,"A2_benchmark_auto")
print(auto_anoms_1)

auto_anoms_2 = auto_osESD(df,"A2_benchmark_auto", size=[20,50,100,150],conditions=[True,False],alphas=[0.001,0.01],weights=[0,0,1,0],learning_length=0.3)
print(auto_anoms_2)

Versions

Python = 3.8.16    
argparse = 1.1  
numpy = 1.24.3  
pandas = 1.5.3  
torch = 1.13.1  
matplotlib = 3.7.0  
scikit-learn = 1.2.1  
scipy = 1.10.1  
rrcf = 0.4.4  

License

License :: OSI Approved :: MIT License
Operating System :: OS Independent

Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea 
and the National Research Foundation of Korea (NRF-2018R1A5A7059549). 
This work was also supported by `Human Resources Program in Energy Technology' 
of the Korea Institute of Energy, Technology Evaluation and Planning (KETEP), 
and was granted financial resources from the Ministry of Trade, 
Industry $\&$ Energy, Republic of Korea (No. 20204010600090).

Related research and tests were mainly done in 
Intelligent Data Systems Laboratory in Hanyang University, Seoul, South Korea.

References

Paper (currently under revision).