b.
Examine the accuracy of the forecast given by each model ( four models: MA 3, MA 5,
Simple Exponential smoothing and Holt's Method) by calculating the root mean square
error (RMSE) for each during the historical period.
ES: 28.23922135
HOLTS: 41.13729597
MA3:29
MA5: 28.2491499
e. Which model does minimize the RMSE? Carefully explain which characteristics of the
original data caused one of these models to minimize the RMSE.
For this data, the simple exponential smoothing method resulted in the lowest RMSE. This
suggest that SES is well suited for the data we have. One possible reason this could be is because
SES is specifically designed for time series data, where values are chronologically ordered. And
this data represents a time series, SES captures the underlying patterns and trends effectively.
f. Using Holt's method forecast 12 months of thermostat sales for 2017.
