Time Series Forecasting with Facebook’s Prophet: A Complete Guide
Introduction
Time series forecasting is an essential tool in various industries such as finance, energy, and transportation. Facebook’s Prophet is a powerful and easy-to-use library that enables us to forecast time series data effectively. In this article, we will dive into the details of the Prophet model, understand its underlying concepts, and explore a practical example using the AirPassengers dataset. We will also explain each line of the provided Python code to give you a better understanding of how to use the Prophet library.
- The Prophet Model
Facebook’s Prophet is an open-source library for time series forecasting, which is built on top of Stan, a probabilistic programming language. The model is designed to handle the complexities of time series data, such as seasonality, trends, and holidays, with minimal manual adjustments. It is robust to missing data, outliers, and can handle data with multiple seasonal patterns. The flexibility and simplicity of Prophet make it suitable for a wide range of forecasting applications.
The Prophet model is an additive model that combines three main components:
a. Trend: The overall direction of the time series data, either increasing or decreasing. The trend can be linear or logistic.
b. Seasonality: The repeating patterns in the time series data. Seasonality can be yearly, weekly, or daily, depending on the dataset.
