Univariate Anomaly Detection Module

Build

$ docker build . -t univariate-anomaly-detection

Run

$ docker run -p 8080:8080 univariate-anomaly-detection

Unit Tests

Run with python 3.9.12+(>=12)

$ python3.9 -m venv .
$ source bin/activate
$ python -m pip install --upgrade pip
$ python -m pip install -r requirements.txt
$ cd src/tests
$ pytest test_api.py

Documentation

• Swagger: http://localhost:8080/documentation
• ReDoc: http://localhost:8080/redoc

Use

1. detect-point-anomalies

$ curl -X 'POST' \
  'http://localhost:8080/detect-point-anomalies' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "train_data": {
        "1379980800000": 55620.0,
        "1379981100000": 55800.0,
        "1379981400000": 56160.0,
        "1379981700000": 55620.0,
        "1379982000000": 55530.0,
        "1379982300000": 55530.0
    },
    "score_data": {
        "1382400000000": 90540.0,
        "1382400300000": 90720.0,
        "1382400600000": 89910.0,
        "1382400900000": 87390.0,
        "1382401200000": 85410.0,
        "1382401500000": 79650.0
    },
    "parameters": {
        "c": 3,
        "window": "15T"
    }
}'

returns

{
    "anomaly_list": {
        "1382400000000": false,
        "1382400300000": false,
        "1382400600000": true,
        "1382400900000": true,
        "1382401200000": true,
        "1382401500000": true,
        "1382401800000": true
    }
}

2. detect_threshold_anomalies

$ curl -X 'POST' \
  'http://localhost:8080/detect-threshold-anomalies' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
		"score_data": {
			"1609455600": 0.12178372709024772, 
			"1609455660": 0.11050720099031877, 
			"1609455720": 0.10551539379110654, 
			"1609455780": 0.12782051546031659, 
			"1609455840": 0.10162464965348023, 
			"1609455900": 0.10842426724768395, 
			"1609455960": 0.11646994268581218, 
			"1609456020": 0.1069694857691467, 
			"1609456080": 0.10106735409516875, 
			"1609456140": 0.10532371366814815}, 	
		"parameters": {
			"high": 0.11050720099031877}
}'

returns

{
  "anomaly_list": {
    "1609455600": true,
    "1609455660": false,
    "1609455720": false,
    "1609455780": true,
    "1609455840": false,
    "1609455900": false,
    "1609455960": true,
    "1609456020": false,
    "1609456080": false,
    "1609456140": false
  }
}

3. detect_levelshift_anomalies

$ curl -X 'POST' \
  'http://localhost:8080/detect-levelshift-anomalies' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "score_data": {
        "9799000": 2.119156122094594,
        "9800000": 2.170792081232256,
        "9801000": 2.072264556251881,
        "9802000": 2.048970442156524,
        "9803000": 2.0344976627265456,
        "9804000": 2.030428751714349,
        "9805000": 1.995015841410387,
        "9806000": 1.975725357663717,
        "9807000": 1.96430842737761,
        "9808000": 1.944965381356912,
        "9809000": 1.9299464555495665
        },
        "parameters": {
            "c": 1,
            "window": "3S"
        }
}'

returns

{
    "anomaly_list": {
            "9799000": false,
            "9800000": false,
            "9801000": true,
            "9802000": false,
            "9803000": false,
            "9804000": false,
            "9805000": false,
            "9806000": false,
            "9807000": false,
            "9808000": false,
            "9809000": false
        }
}

4. detect_volatilityshift_anomalies

$ curl -X 'POST' \
  'http://localhost:8080/detect-volatilityshift-anomalies' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "score_data": {
        "9799000": 2.119156122094594,
        "9800000": 2.170792081232256,
        "9801000": 2.072264556251881,
        "9802000": 2.048970442156524,
        "9803000": 2.0344976627265456,
        "9804000": 2.030428751714349,
        "9805000": 10.995015841410387,
        "9806000": 1.975725357663717,
        "9807000": 1.96430842737761,
        "9808000": 1.944965381356912,
        "9809000": 1.929946455549566
        },
        "parameters": {
            "c": 1,
            "window": "3S"
        }
}'

returns

{
    "anomaly_list": {
        "9799000": false,
        "9800000": false,
        "9800000": false,
        "9801000": false,
        "9802000": false,
        "9803000": false,
        "9804000": true,
        "9805000": true,
        "9806000": false,
        "9807000": false,
        "9808000": false,
        "9809000": false
    }
}

Add the parameter aggregate_anomalies

$ curl -X 'POST' \
  'http://localhost:8080/detect-point-anomalies' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "train_data": {
        "1379980800000": 55620.0,
        "1379981100000": 55800.0,
        "1379981400000": 56160.0,
        "1379981700000": 55620.0,
        "1379982000000": 55530.0,
        "1379982300000": 55530.0
    },
    "score_data": {
        "1382400000000": 90540.0,
        "1382400300000": 90720.0,
        "1382400600000": 89910.0,
        "1382400900000": 87390.0,
        "1382401200000": 85410.0,
        "1382401500000": 79650.0
    },
    "parameters": {
        "c": 3,
        "window": "15T",
        "aggregate_anomalies": "500S"
    }
}'

returns

{
    "anomaly_list": {
        "1382400000000": false,
        "1382400300000": false,
        "1382400600000": true,
        "1382400900000": false,
        "1382401200000": false,
        "1382401500000": false,
        "1382401800000": false
    }
}

Note

• The parameters window and aggregate_anomalies expect offset aliases from the following list https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
• Here, the unit for timestamp is milliseconds.

New Release

1. Update __version__ in src/main.py with a new commit.
2. Tag this commit.