Outlier Detection

This repository contains the example code talked about in this application note, implementing a method of detecting outliers in multiple Disruptive Technologies (DT) Wireless Temperature Sensor timeseries using DBSCAN clustering. Written in Python 3, it uses the DT Python API to communicate with a DT Studio project and its sensors.

Before Running Any code

A DT Studio project containing temperature sensors should be made. Sensors with the label 'outlier_detection' will be fetched by the example code.

Environment Setup

Dependencies can be installed using pip.

pip3 install -r requirements.txt

Using your authentication details, set the following environment variables. Information about setting up your project for API authentication can be found in this streaming API guide.

export DT_SERVICE_ACCOUNT_KEY_ID='<YOUR_SERVICE_ACCOUNT_KEY_ID>'
export DT_SERVICE_ACCOUNT_SECRET='<YOUR_SERVICE_ACCOUNT_SECRET>'
export DT_SERVICE_ACCOUNT_EMAIL='<YOUR_SERVICE_ACCOUNT_EMAIL>'

Usage

Provide a Project ID to fetch data from all labeled temperature devices.

python3 main.py <PROJECT_ID>

Use the -h flag to print additional flags available.