Скачать или смотреть How to Create ARIMA Models on Multiple Tsibbles in R Using purrr

Discover an effective method to create ARIMA models for time series data in R, specifically when working with multiple tsibbles in a tibble structure.
---
This video is based on the question https://stackoverflow.com/q/74921138/ asked by the user 'Shawn Way' ( https://stackoverflow.com/u/9632990/ ) and on the answer https://stackoverflow.com/a/74921399/ provided by the user 'stefan' ( https://stackoverflow.com/u/12993861/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Performing modeling on multiple tsibbles in a tibble

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Create ARIMA Models on Multiple Tsibbles in R Using purrr

Modeling time series data can be complex, especially when dealing with multiple datasets. A common challenge in R, particularly with the tidyverse ecosystem, is how to efficiently create statistical models for each dataset contained in a tibble. In this guide, we will break down the process of creating ARIMA models for time series data stored in tsibbles, using the powerful mapping functions in the purrr package.

The Problem Statement

Imagine you have a set of time series data formatted as a tibble and tsibble, and you want to create ARIMA models from this data. You might find yourself facing issues with purrr when trying to map your modeling function across a list column containing tsibbles. This frustration is common when working with multiple time series datasets, especially if you have a considerable number of them, like 70 data sets in this scenario.

Understanding the Structure

Let’s start by reviewing the tibble structure you’re working with. Here’s a simple representation:

Formula IDFormula Namedatadata_ts1Flubber2 variables2 variablesdata: A standard tibble containing two columns (Date, Value).

data_ts: A tsibble that allows for time series modeling.

The Solution Explained

To estimate an ARIMA model effectively, you need to use the model() function. This function is different from what you might be attempting with purrr to loop over a list column. Here’s how to correctly implement it:

Step-by-Step Implementation

Load the Required Libraries: Ensure you have the necessary libraries loaded.

[[See Video to Reveal this Text or Code Snippet]]

Correctly Map the Model: Use the map function but make sure to provide a single dataset to model(). Here's how you can modify your code:

[[See Video to Reveal this Text or Code Snippet]]

Reporting the Model Results: After mapping your models, you can use map(report) to visualize the results.

[[See Video to Reveal this Text or Code Snippet]]

Sample Output

After running the above code, your output might look like this:

[[See Video to Reveal this Text or Code Snippet]]

This output indicates a successful modeling with the specified order of the ARIMA model.

Conclusion

Modeling for multiple tsibbles in R can be streamlined using the purrr package when you employ the model() function correctly. By following the above steps, you can efficiently forecast from your multiple time series datasets. Don’t hesitate to leverage the power of R and tidyverse to handle your data modeling challenges!



Now that you have the tools and understanding to create ARIMA models effectively, it’s time to apply these techniques to your datasets. Happy modeling!