Git Product home page Git Product logo

timeserieseventdetection_datascience's Introduction

TimeSeriesEventDetection_DataScience

This script analyzes a timestamped power consumption signal for an electrical device to detect active usage start/stop times.

Here are the steps taken to detect the start/stop time of active usages:

1- Denoising the raw signal: A butterworth lowpass filter is designed and the raw signal is filtered out to eliminate the high-frequency noises.

Challenges: cutoff frequency has to be selected carefully in order to keep important information of the signal specially signal edges which also consists of high-frequency components.

2- Finding trend of the signal: This step is done for detecting the edges.

Challenges: 1- Size of first derivative of the signal is decreased by 1 that must be correctly compensated for finding the corresponding timestamp. 2- To completely denoise the signal remained from the first step due to limitation on cutoff frequency, signal smoothness must be done by specifying a proper threshold value.

3- To indicate the potential usage periods, non-zero values of the first derivative of the signal get 1 and zero values get 0 as an on/off signal.

Challenges: There are many false detections that must be still eliminated.

4- Matched filter method is used in detecting the true usuage periods and removing false detections. A typical usage pattern is defind and convolved with the potential usage period signal to detect the true usage periods. Sign function is applied to show the result as an on/off signal.

Challenges: 1- Defining the kernel for matched filter, i.e., typical usage pattern, is always tricky and has to be done carefully by studying the typical duration of true active usage periods.

5- Finally, derivative of the usage on/off signal is taken to indicate start/stop time as a separate signal.

Challenges: 1- Size of first derivative of the signal is decreased by 1 that must be correctly compensated for finding the corresponding timestamp. 2-Please note that always the first couple of samples due to filtering and taking derivatives are not reliable that must be eliminated from the final results.

timeserieseventdetection_datascience's People

Contributors

sajjadamini avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.