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Recursive least squares

Recursive least squares (RLS) is a statistical method used to estimate the parameters of a model as new data becomes available. Imagine you’re trying to predict the price of a stock based on several factors. Instead of recalculating everything from scratch each time you get new data, RLS updates the estimates quickly and efficiently. It does this by using previous estimates and applying a formula that incorporates the new information, maintaining accuracy while saving time and computation. This approach is particularly useful in dynamic environments where data continuously flows in, like in finance or signal processing.

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    Recursive Least Squares (RLS) is an adaptive algorithm used to continuously update predictions or estimates based on new data. Imagine trying to refine a recipe: each time you make a dish, you adjust the ingredients based on how it turned out. RLS works similarly by using past data to refine its estimates about a system's behavior, minimizing the difference between predicted and actual outcomes. As new information arrives, it recalculates to improve accuracy without starting from scratch, making it efficient for real-time applications like signal processing or financial forecasting.