Hands-On Guide to TadGAN (With Python Codes)

Hands-On Guide to TadGAN (With Python Codes)

In this analysis, we are going to discuss practical implementation of TadGAN through Orion. Orion is a machine learning Python-based library for unsupervised time series anomaly detection. This toolkit provides various verified pipelines known as Orion pipelines and uses various AutoML tools developed under DATA TO AI at MIT.

In this analysis, we are going to discuss practical implementation of TadGAN through Orion. Orion is a machine learning Python-based library for unsupervised time series anomaly detection. This toolkit provides various verified pipelines known as Orion pipelines and uses various AutoML tools developed under DATA TO AI at MIT.

A nomaly Detection techniques have been widely used in data science and now with the rapid increase in temporal data, there has been a huge surge of researchers who are developing new algorithms dealing with outliers across this domain. The time series anomaly detection concentrates to isolate anomalous subsequences of varied lengths. Various statistical methods, supervised and unsupervised methods have already been developed and deep learning methods are the ones which stand out the most as they are extremely capable of dealing with non-linearity and have good learning capacity. But one of the major drawbacks of it is that they have extraordinary potential to fit the data including the outliers.

On the other side, Generative Adversarial Network(GAN) are the generating models, they are known for fully capturing the data’s hidden distribution, but they are not successful learners. Recently, a group of researchers from MIT came up with an idea of Time Series Anomaly Detection using Generative Adversarial Networks(TadGAN)- combining deep learning based approaches and GAN approaches together and developed a benchmarking system for Time Series Anomaly Detection. You can read more about the algorithmic part, here.

Lets dig in!

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