There are many good quotes about the hopelessness of predicting the future, and yet I can’t help wanting to conclude this book with some thoughts about what’s coming.
Because time series forecasting has fewer expert practitioners than other areas of data science, there has been a drive to develop time series analysis and forecasting as a service that can be easily packaged and rolled out in an efficient way. For example, and as noted in Chapter 16, Amazon recently rolled out a time series prediction service, and it’s not the only company to do so. The company’s model seems deliberately general, and it frames forecasting as just one step in a data pipeline (see Figure 17-1).
These forecasting-as-a-service modeling endeavors aim for a good enough general model that can accommodate a variety of fields without making terribly inaccurate forecasts. Most of them describe their models as using a mix of deep learning and traditional statistical models. However, because the service is ultimately a black box, it will be difficult to understand what could make forecasts go wrong or even to retrospectively investigate how they can be improved. This means there is a reasonably high quality level for the forecasts but probably a performance ceiling as well.
This service can be valuable for companies that need many forecasts but do not have the personnel available to generate them individually. However, for companies that have ...