Time series forecasting can be challenging as there are many different methods you could use and many different hyperparameters for each method.
The Prophet library is an open-source library designed for making forecasts for univariate time series datasets. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make skillful forecasts for data with trends and seasonal structure by default.
In this tutorial, you will discover how to use the Facebook Prophet library for time series forecasting.
After completing this tutorial, you will know:
- Prophet is an open-source library developed by Facebook and designed for automatic forecasting of univariate time series data.
- How to fit Prophet models and use them to make in-sample and out-of-sample forecasts.
- How to evaluate a Prophet model on a hold-out dataset.
Let’s get started.
Time Series Forecasting With Prophet in Python
Photo by Rinaldo Wurglitsch, some rights reserved.
Tutorial Overview
This tutorial is divided into three parts; they are:
- Prophet Forecasting Library
- Car Sales Dataset
- Load and Summarize Dataset
- Load and Plot Dataset
- Forecast Car Sales With Prophet
- Fit Prophet Model
- Make an In-Sample Forecast
- Make an Out-of-Sample Forecast
- Manually Evaluate Forecast Model
Prophet Forecasting Library
Prophet, or “Facebook Prophet,” is an open-source library for univariate (one variable) time series forecasting developed by Facebook.
Prophet implements what they refer to as an additive time series forecasting model, and the implementation supports trends, seasonality, and holidays.
Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects
#time series #prophet #python
