Beginning Anomaly Detection Using

 art_daily_perfect_square_wave

This data set has no noise or anomalies and is a normal time series dataset. As you can 

see below, the time series has values at different timestamps.

Using visualization, you can plot the time series now. You convert the timestamp to 

datetime for this work and also drop the timestamp column. As shown below, the time 

series shows the datatime vs. the value column.

Dataset: art_daily_perfect_square_wave.csv

Figure 6-44.  A graph showing anomalies

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Figure 

6-45

 shows the code to generate a graph showing the time series.

Figure 6-45.  A graph showing the time series

Let’s add the anomaly column to the original dataframe and prepare a new 

dataframe. Using visualization, you can plot the new time series now. As shown below, 

the time series shows the datatime vs. value column. Since there are no anomalies, 

everything is green. Figure 

6-46

 shows the code to generate a graph showing anomalies.

Figure 6-46.  A graph showing anomalies

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Since this data set has no noise or anomalies and is a normal time series dataset, 

there are no anomalies (datapoints in RED) shown and everything is green.

Next, let’s examine another dataset which is different from the current dataset. You 

will build a LSTM model and see if there are anomalies or not.