Python Prophet Tutorial

Python Prophet is an open-source forecasting library developed by Facebook (now Meta) for time series data analysis. It helps us predict future trends using historical data such as sales, website traffic, or temperature. Prophet is built on top of python and is designed to be simple, flexible and accurate for business forecasting.

What is Time Series Forecasting?

Time series forecasting is the process of predicting future values based on previously observed data. It is commonly used in fields like finance, retail, and weather prediction, where data changes over time.

Why Choose Prophet for Forecasting?

Prophet is one of the most widely used tools for time series forecasting because it is both powerful and easy to use. It automatically handles missing values, outliers, and seasonality. We can quickly create forecasts with just a few lines of code.

Here are some key advantages of using prophet −

• Ease of Use − We can create forecasts with simple python commands, without deep statistical knowledge.
• Handles Seasonality and Holidays − Prophet can automatically model yearly, weekly, and daily patterns, and we can also add our own custom holidays.
• Scalable − It can work on both small datasets and large-scale forecasting problems.
• Customizable − We can fine-tune trend, seasonality and holiday components based on our data.
• Accurate and Reliable − It produces stable results, even when the data is noisy or incomplete.

How to Use Prophet?

To use prophet, we need to set up our environment, install the library, and import it into our python project. Prophet can be installed using either pip or conda. Once installed, we can prepare our data, train a model, and make future predictions.

Following are the example steps to get started −

• Prepare your dataset with two columns: ds for the date and y for the values you want to forecast.
• Create a Prophet model to work with your data.
• Fit the model to your dataset so it can learn from past trends.
• Make future predictions and visualize the results to see how your data might change over time.

Each of these steps will be explained in detail in the next chapters.

Example of Prophet Forecasting

Here's an example that shows how we can train a prophet model and generate future predictions with just a few lines of code.

from prophet import Prophet
import pandas as pd

# Create a sample dataset
data = {'ds': pd.date_range(start='2023-01-01', periods=30),
        'y': [i + (i*0.05) for i in range(30)]}
df = pd.DataFrame(data)

# Create and fit the model
model = Prophet()
model.fit(df)

# Make future predictions
future = model.make_future_dataframe(periods=10)
forecast = model.predict(future)

print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())

Following shows the output of the program. The actual numeric values may differ slightly each time we run the code.

           ds       yhat  yhat_lower  yhat_upper
35 2023-02-05  30.488273   29.913422   31.050324
36 2023-02-06  31.024856   30.410232   31.583029
37 2023-02-07  31.561472   30.956331   32.146993
38 2023-02-08  32.098081   31.477022   32.674295
39 2023-02-09  32.634690   32.016270   33.226125

We will explain how this code works in detail in the subsequent chapters of this tutorial.

Prerequisite to Learn Prophet

Before starting with Prophet, it is helpful to know basic Python programming, data handling with pandas, and some basic ideas about time series data. You can learn these topics from our free Python and Pandas tutorials.

Getting Started with Prophet

Now that we understand what is prophet and how it works, this tutorial will guide you through installation, setup, and the main concepts of time series forecasting using prophet.

Prophet Fundamentals

Here, we understand how prophet builds forecasts. We'll prepare data, create predictions, and learn about important components like trend, seasonality, and holidays. This section gives us a complete picture of prophet's core concepts.

• Prophet - Data Preparation
• Prophet - Data Format
• Prophet - Create Forecast
• Prophet - Forecast Visualization
• Prophet - Model Components
• Prophet - Trend
• Prophet - Seasonality
• Prophet - Holidays
• Prophet - Additive and Multiplicative Models
• Prophet - How Prophet Works (Architecture & Math)

Prophet Model Settings

This section explains how to adjust prophet settings to improve forecasting results. You'll learn how to handle missing data, detect outliers, fine-tune parameters, and modify seasonality or growth settings.

• Prophet - Growth Models
• Prophet - Missing Data Handling
• Prophet - Outlier Handling
• Prophet - Custom Seasonality
• Prophet - Holiday Adjustments
• Prophet - Changepoints
• Prophet - Parameter Tuning

Advanced Prophet Modeling

Here, we work with advanced forecasting features. You'll learn how to add extra regressors, create hierarchical forecasts, and handle uncertainty intervals to achieve more accurate predictions.

• Prophet - Extra Regressors (Multivariate Forecasting)
• Prophet - Hierarchical Forecasting
• Prophet - Uncertainty Intervals & Confidence Bounds

Prophet Evaluation and Diagnostics

In this section, we focus on measuring prophet's performance. You'll learn to use cross-validation, performance metrics, and backtesting to measure accuracy and compare prophet with other forecasting models.

• Prophet - Cross Validation
• Prophet - Performance Metrics
• Prophet - Backtesting
• Prophet - Model Diagnostics & Interpretation
• Prophet - Improving Accuracy
• Prophet - Comparison with Other Forecasting Models

Prophet Use Cases

This section explains how prophet is used in real-world projects such as sales, demand, energy, and financial trend forecasting.

• Prophet - Sales Forecasting
• Prophet - Demand Forecasting
• Prophet - Web Traffic Forecasting
• Prophet - Energy Forecasting
• Prophet - Financial Forecasting

Integration and Deployment

Once the model is ready, we'll learn how to use it in real projects. You'll learn how to integrate prophet with python libraries, automate forecasting, and deploy models using tools like flask, fastAPI, or streamlit.

• Prophet - Integration with Pandas and scikit-learn
• Prophet - Integration with Dash or Streamlit
• Prophet - Save and Load Model
• Prophet - Automate Forecasts
• Prophet - Build Dashboard
• Prophet - Deploy with Flask / FastAPI
• Prophet - Running Prophet in Production (Scheduling & Monitoring)
• Prophet - Debugging and Troubleshooting Common Errors

Prophet Deployment

In this part, we learn how to take a trained prophet model into practical use. You'll learn how to save and load models, automate forecasts, build dashboards, and handle common issues during deployment.

• Prophet - Save and Load Model
• Prophet - Automate Forecasts
• Prophet - Build Dashboard
• Prophet - Common Issues

FAQs on Prophet

There are some FAQs on Prophet. This section answers them briefly and clearly.

1. What is Prophet used for?

Prophet is used for forecasting future trends or values based on historical time series data. It helps predict metrics like sales, website traffic, temperature, or stock prices with high accuracy.

2. Is Prophet open source?

Yes, Prophet is an open-source forecasting tool developed by Meta (Facebook). It is freely available and can be easily installed using Python or R.

3. Do I need prior knowledge of machine learning to use Prophet?

No, Prophet is designed for users who may not have a background in machine learning. You can create forecasts using just a few lines of code without needing complex algorithms.

4. Can Prophet handle daily, weekly, and yearly data?

Yes, Prophet can automatically detect and model multiple seasonal patterns, such as daily, weekly, monthly, and yearly trends, making it highly flexible for different types of data.

5. Which programming languages support Prophet?

Prophet currently supports both Python and R, making it accessible for a wide range of developers and analysts.

6. Can Prophet handle missing or irregular data?

Yes, Prophet is designed to handle missing data points and irregular time intervals effectively. It can fill gaps and still produce accurate forecasts.

7. How does Prophet deal with holidays or special events?

Prophet allows users to include custom holiday or event effects in their forecasts. This helps improve accuracy for time series data affected by specific dates or occasions.

8. Can Prophet be used for real-time forecasting?

Prophet is primarily designed for batch forecasting rather than real-time prediction. However, it can be integrated into automated systems to update forecasts regularly.