Summary
Hopefully, you experienced no issues installing Prophet on your machine at the beginning of this chapter. The potential challenge of getting the Stan dependency installed is greatly eased by using the Anaconda distribution of Python. After installation, we looked at the carbon dioxide levels measured in the atmosphere two miles above the Pacific Ocean, at Mauna Loa in Hawaii. We built our first Prophet model and, in just 12 lines of code, were able to forecast the next 10 years of carbon dioxide levels.
After that, we inspected the forecast DataFrame and saw the rich results that Prophet outputs. Finally, we plotted the components of the forecast - the trend, yearly seasonality, and weekly seasonality, to better understand the data's behavior.
There is a lot more to Prophet than just this simple example, though. The remainder of this book will be spent demonstrating all of the parameters and additional features available that allow you to have greater control over...
