ARIMA Models in R
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Korshub's take
Mastering Time Series Forecasting
If you are looking to move beyond basic data analysis and dive into predictive modeling, this course provides a structured path into the world of ARIMA. By leveraging the R stats and astsa packages, you will transition from understanding the fundamental nature of time series to implementing complex seasonal models.
What You'll Gain
- Foundational Knowledge: Learn to explore time series data using R's built-in statistical tools.
- Model Fitting: Progress from fitting ARMA models on simulated data to applying ARIMA models to real-world datasets.
- Validation & Forecasting: Master the essential skills of verifying model validity and generating future forecasts.
- Seasonal Analysis: Extend your expertise to include seasonal data forecasting.
This is an ideal starting point for beginners who want to gain practical, hands-on experience with industry-standard R packages for time series analysis.
About this course
In this course, you will become an expert in fitting ARIMA models to time series data using R. First, you will explore the nature of time series data using the tools in the R stats package. Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa. Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data. Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.
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