Facebook Prophet is an open-source Python library offering an intuitive, automated approach to capturing trends, seasons and exceptional events in time series. Discover why this Machine Learning-based tool has revolutionized predictive data analysis!
One of the great recent technological advances is the ability to predict future trends from historical data.
This is one of the main benefits of Data Science, and its applications are numerous. It can be used to forecast the weather, or the performance of a stock on the stock market.
It’s also a precious asset in e-commerce, for anticipating fashions, and in healthcare, for diagnosing serious illnesses at an early stage.
To accomplish these feats, the secret is time-series forecasting. However, this is a complex process even for the greatest statistical experts. To simplify the task, Meta has created the Facebook Prophet tool.
What is a time series?
A time series is a collection of chronologically ordered data. Within such a dataset, each observation corresponds to a specific moment in time, in the form of a continuous temporal sequence, often characterized by fluctuations such as trends or seasons.
Trends represent long-term evolution. They can be upward, downward or stable, and can be influenced by economic, demographic or environmental factors.
Seasons, on the other hand, are regular or periodic variations that recur at fixed intervals. For example, during the Christmas season, gift sales increase sharply. This is annual seasonality.
This data is used to forecast sales, weather or financial performance. However, modeling them is a tricky business, due to a number of specific characteristics.
For example, “noise” refers to the random and unpredictable fluctuations present in time series. This can be due to external factors or measurement errors.
In the past, time series forecasting relied on traditional methods such as ARIMA (AutoRegressive Integrated Moving Average) models. Their implementation was difficult and required statistical expertise. In 2017, Facebook changed all that.
What is Facebook Prophet?
Developed by Sean J. Taylor and Ben Letham, Facebook Prophet is an open-source library designed to provide an accessible, high-performance solution for time-series forecasting.
It is particularly aimed at users without statistical expertise, thanks to its automation features. Its approach is based on the marriage of traditional methods and modern Machine Learning techniques.
Unlike ARIMA models, which can be difficult to parameterize and don’t always handle complex seasons well, Prophet is far simpler and more powerful.
It is based on an additive decomposition model, separating the time series into three main components: trend, seasonality and vacations (i.e. exceptional events).
The presence of a trend component enables it to handle long-term evolutions, while the seasonality model helps capture periodic variations. What’s more, public holidays and other events are automatically integrated to improve prediction accuracy!
How does it work?
Facebook Prophet works in three main stages. First, the pre-processing phase consists of cleaning and preparing the data.
The tool automatically handles missing values, anomalies and outliers. Users can therefore remain focused on understanding the time series, rather than performing these tasks manually.
The modeling then takes into account trend and seasonality to estimate the components associated with each time observation.
Each season can be daily, weekly, monthly, annual or customized according to context. After this estimation, Prophet forecasts for the desired future period.
Advantages and disadvantages
One of the main strengths of Facebook Prophet is, of course, its ease of use. Even non-technical users can easily exploit it to make quick and effective predictions.
This tool therefore democratizes time series modeling for a wider audience. What’s more, the automatic handling of exceptional events is a valuable asset.
This allows seasonal variations to be taken into account, improving the accuracy of predictions during special periods.
And for complex time series with multiple levels of seasonality, Prophet is ideal for managing multiple seasons. This makes it a versatile solution for a wide variety of applications.
Nevertheless, it’s important to be aware of the limitations of this solution. Like any statistical model, it can be influenced by outliers that distort predictions.
This is why data pre-processing is essential to minimize this effect. In addition, the tool may have difficulty in capturing the more complex, non-linear dependencies between variables…
What are the fields of application?
Since its launch, Facebook Prophet has made a name for itself in the e-commerce industry. Based on past sales data, seasonal trends and special events such as promotions or sales, this Python library can provide accurate forecasts.
This enables companies to better manage their inventories, plan their marketing campaigns and anticipate peaks in activity.
Similarly, in the financial sector, Prophet has changed everything by making it possible to analyze historical revenue and profit data to identify trends and seasons.
With this crucial performance forecasting information, decision-makers are able to anticipate periods of growth and slowdown for budget planning and investment choices.
Investors and other financial institutions can also use it to predict fluctuations in share prices, exchange rates or commodity prices. In this way, they can minimize risk.
In the healthcare sector, the tool can be used to predict time series of medical data. This includes, for example, hospital admissions, medical consultations and infection rates.
As a result, hospitals can better plan their resources, anticipate peak activity periods, and ultimately provide better patient care.
Conclusion: Facebook Prophet, time series forecasting accessible to all
Thanks to its extreme simplicity, Facebook Prophet has enabled a wide audience to exploit time series forecasting.
The tool is used by Data Science experts and business users alike, offering them a myriad of new possibilities.
As an open-source library, it continues to be improved over time by researchers and developers. We can therefore expect it to continue to occupy an essential place in the field of predictive analytics.
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Now you know all about Facebook Prophet. For more information on the same subject, take a look at our Machine Learning dossier!
