March 10th, 2024

What is FBProphet?

By Zach Fickenworth · 6 min read

FB prophet time series forecasting

Overview

In the vast world of time series forecasting, a standout tool has emerged from Facebook’s data science arsenal: Prophet. Developed by Facebook’s Core Data Science team and released in 2017, this algorithm is built to decipher the intricacies of time series data effectively, making it a prime choice for a myriad of applications, from e-commerce sales projections to meteorological predictions.

Visualization of FB Prophet forecast

How Does Prophet Work?

At its heart, Prophet works by decomposing time series data into three fundamental elements:

Trend: The general direction your data is heading, like sales increasing over time. In order to recognize the overarching trajectory of your data, Prophet employs a piecewise linear regression to ascertain whether the data is on an upward or downward trajectory.

Seasonality: Regular, cyclical ups and downs in data, like the spike in sales every holiday season. Be it daily, weekly, or monthly, there are often cycles underlying your data. Prophet harnesses the Fourier series to capture these oscillations, accounting for varying seasonal trends.

Noise: Random blips or changes that don’t fit the trend or seasonality, like a promotional event causing a one-time spike. Prophet identifies these random inconsistencies and fluctuations, ensuring they don’t distort the forecast.
Bayesian framework
The algorithm automatically takes into consideration these three underlying components when generating a forecast, making the procedure accurate off-the-shelf, adaptable, and extremely user-friendly. Furthermore, by leveraging a Bayesian framework, Prophet goes beyond single-value, point forecasts. It provides a probabilistic perspective, offering a measure of uncertainty around the main forecast.

When Should Prophet Be Your Go-To?


For Beginners: With its intuitive design, Prophet is friendly to those unfamiliar with time series forecasting. Its parameters are set to be easily understood even if you’re not a statistical modeling veteran.

Mean Shifts in Data: If your data experiences significant disruptions or mean shifts, Prophet is adeptly equipped to tackle these variations, giving you a clear forecast despite the hiccups.

However, Prophet Might Not Always Be Ideal

While Prophet’s ease of use and adaptability are commendable, there are scenarios where it might not be the best choice:

Maximized Predictive Performance: In contexts where even minor enhancements in forecasting precision can translate to substantial business value, classical time series models like ARIMA or exponential smoothing might outdo Prophet in accuracy and be a better choice.

In summation, while numerous methodologies exist for time series forecasting, Prophet is distinguished by its blend of precision, adaptability, and user-friendliness. Whether you’re looking to forecast e-commerce sales, predict website traffic, or get a sense of future stock market movements, Prophet has you covered — easily try it out within Julius today: Julius AI.

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