Creating a VAE for time series anomaly detection

Building on the foundation laid in the previous recipe, we now turn our attention to VAEs, a more sophisticated and probabilistic approach to anomaly detection in time series data. Unlike traditional AEs, VAEs introduce a probabilistic interpretation, making them more adept at handling inherent uncertainties in real-world data.

Getting ready

This code in this recipe is based on PyOD. We also use the same dataset as in the previous recipe:

N_LAGS = 144
series = dataset['y']

Now, let’s see how to create a VAE for time series anomaly detection.

How to do it…

We begin by preparing our dataset, as in the previous recipe:

1. The dataset is first transformed using a sliding window, a technique that helps the model understand temporal dependencies within the time series:

   import pandas as pd
   from sklearn.preprocessing import StandardScaler
   import numpy as np
   input_data = []
   for i in range(N_LAGS, series...