Abdul Majed Raja walks us through an example using the `anomalize`

package:

One of the important things to do with Time Series data before starting with Time Series forecasting or Modelling is Time Series Decomposition where the Time series data is decomposed into Seasonal, Trend and remainder components. anomalize has got a function

`time_decompose()`

to perform the same. Once the components are decomposed,`anomalize`

can detect and flag anomalies in the decomposed data of the reminder component which then could be visualized with`plot_anomaly_decomposition()`

.btc_ts %>% time_decompose(Price, method = "stl", frequency = "auto", trend = "auto") %>% anomalize(remainder, method = "gesd", alpha = 0.05, max_anoms = 0.2) %>% plot_anomaly_decomposition()As you can see from the above code, the decomposition happens based on ‘stl’ method which is the common method of time series decomposition but if you have been using Twitter’s AnomalyDetection, then the same can be implemented in anomalize by combining time_decompose(method = “twitter”) with

`anomalize(method = "gesd")`

. Also the ‘stl’ method of decomposition can also be combined with`anomalize(method = "iqr")`

for a different IQR based anomaly detection.

Read on to see what else you can do with `anomalize`

.