Hi, my name is Kenneth, I am working with Ignition Machine Learning Manager and I have historical tag data (temperature and humidity) collected over several months.
My goal is to build a model that can predict future values (for example, predicting temperature 10 minutes ahead) based on current and past data.
Currently, I am able to:
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Retrieve tag history data
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Train a regression model (OLS Multiple Linear Regression)
However, I am unsure about the correct way to structure the dataset for time-based predictions.
So I am working and creating models with this Ignition Machine Learning Manager What type of algorithm can I use to train a model entirely within Ignition, without depending on external systems?
What I need is to create a trained model that can be read directly by Ignition or through a tag, and use it to visualize or predict values some time into the future—for example, 10 minutes ahead of real-time data.
I believe this could be achieved using the Machine Learning Manager by creating and deploying a model within Ignition itself. Could you please guide me on the best approach or recommended algorithms for this use case?
Any help would be greatly appreciated. Thank you in advance.
