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In today's data-driven world, accurate forecasting is crucial for businesses, organizations, and individuals alike. Whether you're predicting sales, website traffic, or any other time-dependent phenomena, having a reliable forecasting model can greatly improve your decision-making process. Enter Prophet, a powerful and intuitive time series forecasting tool developed by Facebook's Core Data Science team.
What is Prophet Model?
Prophet 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 works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.
Prophet can be considered a nonlinear regression model, of the form
Yt = g(t) + s(t)+ h(t) + εt,
were
g(t) describes a piecewise-linear trend (or “growth term”),
s(t) describes the various seasonal patterns,
h(t) captures the holiday effects, and
εt is a white noise error term.
The knots (or changepoints) for the piecewise-linear trend are automatically selected if not explicitly specified. Optionally, a logistic function can be used to set an upper bound on the trend.
The seasonal component consists of Fourier terms of the relevant periods. By default, order 10 is used for annual seasonality and order 3 is used for weekly seasonality.
Holiday effects are added as simple dummy variables.
The model is estimated using a Bayesian approach to allow for automatic selection of the changepoints and other model characteristics.
How does Prophet Model work?
At its core, Prophet is an additive regression model that decomposes a time series into four main components: trend, seasonality, holidays, and errors. Let's briefly explore each component:
1. Trend: The overall trend or growth curve in the data, which can be linear or non-linear.
2. Seasonality: Periodic patterns that repeat over a fixed period, such as weekly, monthly, or yearly cycles.
3. Holidays: Specific dates that may cause deviations from the normal pattern, such as holidays or special events.
4. Errors: Residuals or unexplained noise in the data that cannot be captured by the other components.
By combining these components, Prophet can accurately model and forecast time series data with varying levels of complexity.
Advantages and Limitations of Prophet Model.
Advantages:
Easy to use and intuitive interface.
Automatic detection and modeling of seasonal patterns.
Ability to incorporate holidays and special events.
Flexible trend modeling (linear or non-linear).
Provides prediction intervals and uncertainty estimates.
Open-source and well-documented.
Limitations:
Limited to univariate time series forecasting.
Assumptions of additive components and continuity of time series
Difficulty in handling complex or abrupt changes in patterns.
Limited control over model internals and hyperparameters.
Performance may degrade for very long-term forecasts.
Argyle Enigma Tech Labs Used Case: Time Series Forecasting on Expenses using Prophet Model.
Problem Statement: To develop accurate and reliable time series forecasting models for daily expenses and income data using Prophet Model.
1. Data Collection: The daily expenses and income dataset was collected from the user's day-to-day transactions spanning from January 2018 to February 2024. The dataset included the following variables: 'Date,' 'Time,' 'Expense Amount,' and 'Category.'
2. Data Preprocessing: The steps involved in data preprocessing are:
Converting the 'Date' and 'Time' columns into Datetime format.
Dropping all unnecessary variables.
Plotting the time series to check the trend and seasonality.
Identifying outliers and performing necessary imputations.
Resampling the data to daily frequencies.
By completing these steps, the data is prepared for model fitting.
3. Model Fitting:
Renaming the 'Datetime' column as 'ds' and the 'Amount' column as 'y' is a prerequisite for fitting the Prophet Model.
Initializing the Prophet Model to forecast 'Expense Amount' ('y') for 12 months ahead with hourly frequency.
Evaluating the model's goodness of fit using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The objective is to minimize these values to ensure successful model fitting.
Plotting the forecasted values against the actual values to visualize the output.
Deployment of the Prophet Model using Flask: The fitted model is deployed using Flask to provide a better user interface.
Conclusion
Prophet's ease of use and robust capabilities make it a valuable tool for time series forecasting, enabling businesses and individuals to make informed decisions based on accurate predictions. At Argyle Enigma Tech Labs, our use case on forecasting daily expenses demonstrates the practical applications and effectiveness of Prophet in real-world scenarios. By leveraging Prophet, we can confidently navigate the complexities of time-dependent data and uncover actionable insights to drive future success. Whether you're a seasoned data scientist or a business professional, Prophet empowers you to harness the power of forecasting with confidence and precision.
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