Terminé

Modification on a multivariate time series anomaly detection model -- 3

Budget: Not more than $450

This is a simple project where you are required to modify a Graph neural network/Graph deviation network model that is based on ([login to view URL])

The github model is an anomaly detection model. The steps include:

1. Sensor Embedding: uses embedding vectors to capture the unique characteristics of each variable

2. Graph Structure Learning: learns a graph structure representing dependence relationships between variable

3. Graph Attention-Based Forecasting: forecasts future values of each variable based on a graph attention function over its neighbors

4. Graph Deviation Scoring: identifies deviations between actual and predicted values using threshold calculation

Modification on the model:

Apply one of the below to set thresholds and select the best threshold to identify extreme values.

1. nonparametric dynamic error thresholding (NDT) from ([login to view URL])

OR

2. POT (peaks over threshold) model of EVT (extreme value theory) from ([login to view URL])

Implement the model on these datasets:

1. SMD dataset

[login to view URL]

2. MIT-BIH Supraventricular Arrhythmia Database (MBA)

[login to view URL]

This project should incude:

-Exploratory Data Analysis

-Data preprocessing

-Model building and training

-Provide visualization and list of the labelled anomalies in time series

Evaluation metrics: Precision, Recall, AUC, F1

Compétences : Matlab and Mathematica, Python, Algorithme, Statistiques, Machine Learning (ML)

Concernant le client :
( 1 commentaire ) Kuala Lumpur, Malaysia

Nº du projet : #34858930

Décerné à:

faisalnaeem45

I am currently working as a Postdoctoral fellow in the area of machine learning and GAN for resource allocation and cyber-security in wireless networks. I have an international conference and journal publications in hi Plus

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6.4

39 freelances font une offre moyenne de 473 $ pour ce travail

suyashdhoot

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7.3
loyidandrews

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7.0
WycOj

Hi ,I have checked all the requirements in your project of modification on a multivariate time series anomaly detection,I am confident about this project and I am able to deliver the best results within the stated budg Plus

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6.7
AwaisChaudhry

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6.5
dahani9091

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6.0
uzairrzahid

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6.3
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6.0
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4.7
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Actisoft2017

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3.7