Deep Learning for Time Series Anomaly Detection

In this chapter, we’ll delve into anomaly detection problems using time series data. This task involves detecting rare observations that are significantly different from most samples in a dataset. We’ll explore different approaches to tackle this problem, such as prediction-based methods or reconstruction-based methods. This includes using powerful methods such as autoencoders (AEs), variational AEs (VAEs), or generative adversarial networks (GANs).

By the end of this chapter, you’ll be able to define time series anomaly detection problems using different approaches with Python.

The chapter covers the following recipes:

• Time series anomaly detection with Autoregressive Integrated Moving Average (ARIMA)
• Prediction-based anomaly detection using deep learning (DL)
• Anomaly detection using a long short-term memory (LSTM) AE
• Building an AE using PyOD
• Creating a VAE for time series anomaly...