Building an AE using PyOD

PyOD is a Python library that is devoted to anomaly detection. It contains several reconstruction-based algorithms such as AEs. In this recipe, we’ll build an AE using PyOD to detect anomalies in time series.

Getting ready

You can install PyOD using the following command:

pip install pyod

We’ll use the same dataset as in the previous recipe. So, we start with the dataset object created in the Prediction-based anomaly detection using DL recipe. Let’s see how to transform this data to build an AE with PyOD.

How to do it…

The following steps show how to build an AE and predict the probability of anomalies:

1. We start by transforming the time series using a sliding window with the following code:

   import pandas as pd
   from sklearn.preprocessing import StandardScaler
   N_LAGS = 144
   series = dataset['y']
   input_data = []
   for i in range(N_LAGS, series.shape[0]):
       input_data.append(series...