Implementing featurization techniques
Amazon Forecast lets you customize the way you can transform the input datasets by filling in missing values. The presence of missing values in raw data is very common and has a deep impact on the quality of your forecasting model. Indeed, each time a value is missing in your target or related time series data, the true observation is not available to assess the real distribution of historical data.
Although there can be multiple reasons why values are missing, the featurization pipeline offered by Amazon Forecast assumes that you are not able to fill in the values based on your domain expertise and that missing values are actually present in the raw data you ingested into the service. For instance, if we plot the energy consumption of the household with the identifier (ID) MAC002200
, we can see that some values are missing at the end of the dataset, as shown in the following screenshot: