Using GANs for time series anomaly detection
GANs have gained significant popularity in various fields of ML, particularly in image generation and modification. However, their application in time series data, especially for anomaly detection, is an emerging area of research and practice. In this recipe, we focus on utilizing GANs, specifically Anomaly Detection with Generative Adversarial Networks (AnoGAN), to detect time series data anomalies.
Getting ready…
Before diving into the implementation, ensure that you have the PyOD library installed. We will continue using the taxi trip dataset for this recipe, which provides a real-world context for time series anomaly detection.
How to do it…
The implementation involves several steps: data preprocessing, defining and training the AnoGAN model, and finally, performing anomaly detection:
- We start by loading the dataset and preparing it for the AnoGAN model. The dataset is transformed in the same way as...
