Creating a More Efficient Algorithm for Computing Generalized Least Squares Solution
Data like temperature or sales of seasonal products can be seen in periods fluctuating between highs and lows throughout the year. This project is based on finding and computing a more efficient algorithm for generalized least squares solution in a periodic setting. There have been many other algorithms commonly used to compute least squares solutions. However, those algorithms only work well enough for small sets of data and take longer compute as the data sets get larger. We aim to develop an algorithm that will be able to simplify least squares computations by manipulating large sets of data into smaller sets. This can be accomplished by taking a structured matrix for dimension reductions. Our algorithm can be applied to many sets of periodic data used in economics, environmental studies, and engineering practices.
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