Chapter 53: Winter s Method

Often, we must predict future values of a time series such as monthly costs or monthly product revenues. This is usually difficult because the characteristics of any time series are constantly changing. Smoothing or adaptive methods are usually best suited for forecasting future values of a time series. In this section, we describe the most powerful smoothing method: Winter’s method. To help you understand how Winter’s method works, we will use it to forecast monthly housing starts in the United States (U.S.). Housing starts are simply the number of new homes whose construction begins during a month. We begin by describing the three key characteristics of a time series.

Time Series Characteristics

The behavior of most time series can be explained by understanding the following three characteristics: base, trend, and seasonality.

• The base of a series describes the series’ current level in the absence of any seasonality. For example, suppose the base level for U.S. housing starts is 160,000. In this case, we believe that if the current month were an “average” month relative to other months of the year, then 160,000 housing starts would occur.

• The trend of a time series is the percentage increase per period in the base. Thus a trend of 1.02 means that we estimate that housing starts are increasing by 2 percent each month.

• The seasonality (seasonal index) for a period tells us how far above or below a typical month we can expect housing starts to be. For example, if the December seasonal index is .8, then December housing starts are 20 percent below a typical month. If the June seasonal index is 1.3, then June housing starts are 30 percent higher than a typical month.