Unlocking Time Series Forecasting with ARIMA and Prophet

In the world of business analytics, predicting time series data is crucial for anticipating trends in sales, prices, and demand. This course on forecasting using Python dives deep into two of the most popular models: ARIMA and Prophet. You’ll begin your learning journey by grasping the fundamental concepts of time series, including its components—trend, seasonality, and noise. Additionally, you’ll learn how to prepare your data by identifying and fixing outliers, missing values, and duplicates using SQL and Python to work with real sales information.

As you progress, you’ll explore ARIMA modeling, where you’ll understand the important parameters (p, d, q) and leverage auto_arima to validate conditions like autocorrelation, stationarity, and residual normality. You’ll compute vital accuracy metrics such as MAPE and RMSE while visualizing how well your models match real-world data. The course then introduces you to Prophet, a user-friendly tool developed by Meta, designed to simplify the prediction process. You’ll discover how to include external variables (like promotions and prices), manage changepoints, adjust seasonality parameters, and create insightful component graphs to enhance your understanding of the forecasts.

To conclude the course, you will compare both ARIMA and Prophet models using advanced metrics like AIC and BIC, addressing the strengths and weaknesses of each approach. You’ll also learn best practices for presenting forecasts clearly to stakeholders, including confidence intervals and effective visualizations. As your final project, you will automate your predictions by exporting models and scheduling recurring scripts or notebooks. This course integrates theoretical knowledge with practical applications, making it perfect for those eager to excel in forecasting for business.

What you will learn:

• Understand time series concepts, including trend, seasonality, and noise.
• Detect and correct outliers and common issues in business data.
• Build forecasting models with ARIMA and Prophet from scratch.
• Incorporate external variables such as prices or campaigns to enhance predictions.
• Evaluate models using key metrics: MAPE, RMSE, AIC, and BIC.
• Export and automate predictions in scheduled notebooks or scripts.

Course Content:

• Sections: 8 sections
• Lectures: 39 lectures
• Duration: 2h 51m total length

Requirements:

• Basic knowledge of statistics and time series.
• Familiarity with Python and SQL (basic level).
• Have Python installed with libraries like statsmodels, pmdarima, and prophet.
• Understanding of business analytics (optional, but recommended).

Who is it for?

• Data analysts, data scientists, and BI professionals.
• Marketing, sales, and finance teams needing reliable forecasts.
• Students or self-learners interested in machine learning applied to time series.
• Professionals looking to compare classic methods (ARIMA) with modern ones (Prophet).

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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