You can learn how to train and fit the time series forecasting model. Here we are using the model Prophet for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality along with holiday effects. It is also a robust way to handle missing data and shifts in the trend as well as handling outliers in the data. It works best with time series that have strong seasonal effects (Seasonality).

Prophet is open source software released by Facebook’s Core Data Science team. You can download prophet from CRAN and PyPI.

It can be implemented in Python as well as R language.

After completing this guided project you will be able to:

To complete this guided project, you will need a basic understanding of the working mechanics of the Prophet . You will also need some prior experience working with Time Series Analysis to be able to follow our data preprocessing steps easily. It will be more helpful if you have a prior knowledge of Data Visualisation library like Plotly.

This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.

Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.

What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.