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ARIMA Models in R
4.9(313)34k studentsBEGINNEREnglish
Korshub's take
The Korshub Take
This beginner-friendly course teaches time series analysis and forecasting in R using the astsa package. It walks through identifying stationarity, fitting ARMA and integrated ARIMA models, evaluating model validity, and modeling seasonal variations.
Who It's For
R programmers, data analysts, and statisticians who want to master time series forecasting using standard R statistical packages.
Key Takeaways
- Time Series Exploratory Analysis: Analyze time series structures using core functions from R's stats package.
- Astsa Package Modeling: Fit ARMA and ARIMA models to simulated and real-world datasets using the astsa library.
- Model Validation & Forecasting: Check ARIMA model assumptions and generate point and interval forecasts for future values.
- Seasonal ARIMA: Apply seasonal ARIMA configurations to accurately model periodic and seasonal time series data.
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.
Price
Free
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