Time series are one of the most common data types encountered in daily life. Financial prices, weather, home energy usage, and even weight are all examples of data that can be collected at regular intervals. Almost every data scientist will encounter time series in their daily work and learning how to model them is an important skill in the data science toolbox. One powerful yet simple method for analyzing and predicting periodic data is the additive model. This post will walk through an introductory example of creating an additive model for financial time-series data using Python and the Prophet forecasting package developed by Facebook. Along the way, we will cover some data manipulation using pandas, accessing financial data using the Quandl library and, and plotting with matplotlib. To make forecasts, we need to create what is called a future dataframe. We specify the number of future periods to predict (two years) and the frequency of predictions (daily).