As part of an anomaly detection project, I have recently been able to use two very interesting open source products: Prophet released by the Core Data Science team by Facebook and Metaflow, an excellent framework by Netflix. I used Prophet, in a Metaflow flow, to create forecast models of time series. I decided to write this post to share my experience with these two products, creating a small machine learning project.

A small project

Being able to predict the future trend of a time series is very useful in many applications, from the world of finance to sales. For example, we try to predict the direction of the stock market or the correct supply of resources. This post does not set such ambitious goals, but only wants to explore the possibilities offered by Prophet by creating a forecast model that determines the future trend of daily temperatures. To train the model, I used a dataset that collects the minimum daily temperatures over 10 years (1981–1990) in the city of Melbourne, Australia. The source of the data is the Australian Bureau of Meteorology.

The entire source code of the project is available in this git repository

Data exploration

Let’s analyze our dataset with a simple notebook. We use Python and Pandas to load the CSV file.

#aws-batch #metaflow #time-series-forecasting #prophet #aws