Prophet is a forecasting tool designed for analyzing time-series data. It is 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. It was developed by the research team at Facebook and is available as an open-source software.
The History of the Origin of Prophet and the First Mention of It
Prophet was initially developed and released by Facebook’s Core Data Science team in 2017. The primary aim was to provide a tool that could be easily utilized by analysts and developers alike without the need for extensive statistical knowledge. Its implementation in Python and R made it accessible to a broad audience, and it quickly gained popularity in various industries for its ability to handle the challenges of forecasting at scale.
Detailed Information about Prophet: Expanding the Topic
Prophet has become a key tool in time series forecasting, thanks to its flexibility and robustness. The following details expand upon the components of Prophet:
- Trend Model: Identifies underlying trends in the data.
- Seasonality Model: Captures periodic fluctuations in the data, such as daily, weekly, and yearly patterns.
- Holiday Effects: Accounts for holidays or special events that might influence the data.
- Error Term: Considers the random variations that cannot be explained by the model.
Prophet uses an additive model that combines these components, and it incorporates uncertainty intervals to capture uncertainty in the forecasts.
The Internal Structure of the Prophet: How the Prophet Works
The working of Prophet is defined by its additive model that combines different components:
- Trend: Linear or logistic growth trend in time-series.
- Seasonality: Weekly and yearly seasonality with Fourier series.
- Holidays: User-provided list of dates to model effects of holidays or special events.
The model is fit using a variation of the Generalized Additive Model (GAM) framework and uses Stan, a probabilistic programming language for estimation.
Analysis of the Key Features of Prophet
- Robust to Missing Data: Handles missing data points without needing imputation.
- Automatic Detection of Seasonality: Automatically detects seasonal patterns.
- Inclusion of Holidays: Allows for special modeling around holidays and events.
- Flexibility: Offers flexibility in modeling trends and seasonal effects.
- Scalability: Capable of handling large datasets.
Types of Prophet: Table and Lists
There is mainly one type of Prophet model, but it can be configured for different types of growth:
|Linear||Assumes linear growth without any bounds.|
|Logistic||Assumes growth that slows down and hits a saturation point.|
Ways to Use Prophet, Problems and Their Solutions Related to the Use
Prophet can be used for:
- Sales Forecasting
- Stock Market Prediction
- Weather Forecasting
- Traffic Prediction
Problems and Solutions:
- Overfitting: Adjusting seasonality and trend flexibility.
- Inaccurate Holiday Effects: Manually adding important holidays or events.
- Computation Time: Adjusting the seasonality prior scale.
Main Characteristics and Other Comparisons with Similar Terms
|Handling Missing Data||Yes||No||No|
|Ease of Use||High||Medium||Medium|
Perspectives and Technologies of the Future Related to Prophet
Prophet continues to be updated, and the community contributes to its improvement. Future perspectives may include:
- Enhanced algorithms for automatic hyperparameter tuning.
- Integration with real-time analytics platforms.
- Development of specialized versions for particular industries.
How Proxy Servers Can Be Used or Associated with Prophet
Proxy servers like those provided by OxyProxy can be utilized in conjunction with Prophet for web scraping and data collection, particularly for real-time forecasting. By ensuring secure and anonymous access to data, these proxy servers facilitate more accurate and up-to-date predictions.
By considering all these aspects, Prophet emerges as a versatile and powerful tool in time series forecasting, catering to a wide range of applications. Its association with proxy servers further enhances its utility, enabling a more robust data-driven decision-making process.