Forecasting Made Simple: Master ARIMA and Prophet Today

Enroll in this Free Udemy Course on forecasting with ARIMA and Prophet. Limited time offer!

In this course, you’ll dive deeply into the world of time series forecasting, an essential skill for professionals in business analytics. You’ll begin by understanding the foundations of time series: identifying trends, seasonality, and noise. The course provides a comprehensive approach to preparing your data, such as correcting outliers and dealing with null or duplicate values, using SQL and Python to cleanse real sales data.

As you progress, you will learn to model time series data with ARIMA, where you’ll explore its parameters (p, d, q), apply auto_arima for efficient model selection, and validate assumptions like autocorrelation and stationarity. The course emphasizes the use of metrics such as MAPE and RMSE to assess model performance and visualize your results against actual data.

You will also be introduced to Prophet, a forecasting tool developed by Meta that simplifies the prediction process. Discover how to include external variables like promotions, adjust changepoints, and modify seasonalities while creating informative visual components that explain your model’s predictions. Finally, the course culminates with best practices for presenting forecasts that are not only precise but also visually appealing and easy to understand, making you a valued asset in any business environment.

What you will learn:

  • Understand the fundamentals of time series analysis
  • Build predictive models using ARIMA and Prophet
  • Visualize and present forecasting results effectively

Course Content:

  • Sections: 5
  • Lectures: 25
  • Duration: 6 hours

Requirements:

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

Who is it for?

  • Data analysts
  • Data scientists and BI professionals
  • Marketing, sales, and finance teams seeking reliable forecasts
  • Students and self-learners interested in applied machine learning

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