Recursive Least Squares Forgetting

Recursive Least Squares (RLS) algorithms with a forgetting factor allow us to recursively estimate model parameters online while smoothly downweighting older observations. By tracking the decay of its prior, the model continuously adapts to new data, making it particularly suitable for non-stationary environments.

Experiment

Interact with the algorithm step-by-step:

1. Open the Recursive Least Squares Forgetting module.
2. Choose the Data sequence (e.g., "Drift").
3. Adjust the Forgetting factor (e.g., 0.9) and Initial ridge.
4. Change the Steps to observe how the parameters, gains, and the inverse information ellipse update with each new observation. Note how a forgetting factor less than 1 prevents the prior from dominating over time.