Least Squares and Discounted Least Squares in Autoregressive Process
Phongsaphen Jantana, Prapaisri Sudasna-na-Ayudthya
This paper reports on a comparative study between the least squares (LS) method using rank-one update QR factorization and the discounted least squares with direct smoothing. Both approaches are applied to solve the problem of updating estimated parameters in long rolling periods of time-series forecasting using the autoregressive (AR) process. The corresponding model used in the experiment is undamped sinusoidal data with various autoregressive orders. The results of the study indicate that both methods improved effectively as the model order grow. However, the discounted least squares with direct smoothing had a more increasing rate of improvement.
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