Prediction-based anomaly detection using DL

We continue to explore prediction-based methods in this recipe. This time, we’ll create a forecasting model based on DL. Besides, we’ll use the point forecasts’ error as a reference for detecting anomalies.

Getting ready

We’ll use a time series dataset about the number of taxi trips in New York City. This dataset is considered a benchmark problem for time series anomaly detection tasks. You can check the source at the following link: https://databank.illinois.edu/datasets/IDB-9610843.

Let’s start by loading the time series using pandas:

from datetime import datetime
import pandas as pd
dataset = pd.read_csv('assets/datasets/taxi/taxi_data.csv')
labels = pd.read_csv('assets/datasets/taxi/taxi_labels.csv')
dataset['ds'] = pd.Series([datetime.fromtimestamp(x) 
    for x in dataset['timestamp']])
dataset = dataset.drop('timestamp&apos...