Time Series Smoothing by Penalized Least Squares with Applications

This text presents some useful and easy-to-use techniques to estimate trends of time series data without imposing rigid distributional or other typeof assumptions. These techniques offer flexibility, particularly for estimating trends routinely and massively. The book provides some generalizations to the ideas embodied in the time series smoothing problem as orvinally proposed by Víctor M. Guerrero. These generalizations provide some innovative and straightforward ways to calculate trends. The data analyst can objectively choose the desired smoothness, thus enabling camparisons between trends with the same smoothness level for different sample sizes or periodicity of observation. The value added of this book is that it offers the analyst a remarkable flexibility to estimate trends.

• Time series
• Smoothing
• Penalized Least Squares
• Applications
• Probability
• Statistics
• Mathematical Theory