Code and Results for Chapter 3 (Regression and Exponential Smoothing Methods To Forecast Nonseasonal Time Series):

Introduction:
These are results and code for the problems and examples found in Chapter 3 of this famous book.

1. Functions for Exponential Smoothing:
   • simple_exp_smoothing.m (simple exponential smoothing)
   • simple_exp_smoothing_optimum.m (finds the optimal relaxation coefficient when performing simple exponential smoothing)
   • double_exp_smoothing.m (double exponential smoothing)
   • double_exp_smoothing_optimum.m (finds the optimal relaxation coefficient when performing double exponential smoothing)
   • triple_exp_smoothing.m (triple exponential smoothing)
   • triple_exp_smoothing_optimum.m (finds the optimal relaxation coefficient when performing triple exponential smoothing)
2. Examples from the book:
   • example_3_4.m (Duplicates Example 3.4 from the book)
3. Data sets considered in this chapter:
   • load_computer_software_sales.m (loads the computer sales data)
   • load_us_lumber.m (loads the US lumber data set)
   • load_student_enrollment.m (loads the student enrollment data set)
4. Problems:
   • prob_3_4.m (simple exponential smoothing of the U.S. lumber data)
   • prob_3_8.m (plots the forecast weights for the one and two-step ahead)
   • prob_3_9.m (implements Holt's method and double exponential smoothing)
   • prob_3_10.m (computes the variance scale factor for various look-aheads)
   • prob_3_14.m (implements simple/double/triple exponential smoothing on the computer software sales)