As mentioned earlier, the application we will be creating is a stock price estimator. Given a set of stock prices across days, weeks, or years, the forecasting algorithm will internally identify trending patterns. Unlike previous chapters, the application will be architected to be plugged into a production pipeline.
As with previous chapters, the completed project code, sample dataset, and project files can be downloaded from: https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET/tree/master/chapter08.
Exploring the project architecture
Building upon the project architecture and code we created in previous chapters, the architecture we will be exploring in this chapter further enhances the architecture to be more structured and thereby more usable for an end user.
Like in some of the previous chapters, an additional NuGet package—Microsoft.ML.TimeSeries—is required to utilize the forecasting functionality...
