## When should the smoothing constant be large?

The best way to identify your smoothing constant is by understand the difference between a high decimal and low decimal. The smoothing constant is going to be a number between 0 and 1. The higher a smoothing constant, the more sensitive your demand forecast. This means you will see large spikes of data.

**What effect does the value of the smoothing constant?**

The smoothing constants determine the sensitivity of forecasts to changes in demand. Large values of α make forecasts more responsive to more recent levels, whereas smaller values have a damping effect. Large values of β have a similar effect, emphasizing recent trend over older estimates of trend.

**How do you forecast a smoothing constant?**

Pick two successive months and add the figures together and divide by two. This number is the moving average for those two months. Use that figure as your forecast for Month 6. For example, if Month 4 showed 200 sales and Month 5 showed 250 sales, add 200 plus 250 and divide by 2 to get 225.

### What does increasing or decreasing the alpha value do for exponential smoothing?

Alpha. This numeric value, between 0 and 1, controls the calculation. A smaller value (closer to 0) creates a smoother (slowly changing) line similar to a moving average with a large number of periods. A high value for alpha tracks the data more closely by giving more weight to recent data.

**What is smoothing constant?**

The smoothing constant determines the level at which previous observations influence the forecast. These forecasts are compared with the actual observations in the time series and the value of a that gives the smallest sum of squared forecast errors is chosen.

**What is the best smoothing constant?**

α = the smoothing constant, a value from 0 to 1. When α is close to zero, smoothing happens more slowly. Following this, the best value for α is the one that results in the smallest mean squared error (MSE).

#### What effect does the value of the smoothing constant have on the weight given to the past forecast and the last observed value?

It gives a weight of α to the past observation and (1−α) to the past forecast. All the prediction of the time series will be based on the previous predicted value, and be a simple straight line using the first prediction. It will not have any predictive value.

**What factors enter into the choice of a value for the smoothing constant in exponential smoothing quizlet?**

What factors enter into the choice of a value for the smoothing constant in exponential smoothing? The choice of alpha in exponential smoothing depends on how responsive a forecast the manager desires.

**What is exponential smoothing constant?**

Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.

## When using exponential smoothing the smoothing constant is?

When using exponential smoothing, the smoothing constant is typically between . 75 and . 95 for most business applications. indicates the accuracy of the previous forecast.

**What is Alpha in exponential smoothing?**

ALPHA is the smoothing parameter that defines the weighting and should be greater than 0 and less than 1. ALPHA equal 0 sets the current smoothed point to the previous smoothed value and ALPHA equal 1 sets the current smoothed point to the current point (i.e., the smoothed series is the original series).

**Which forecasting technique can place the most emphasis on recent values How does it do this?**

which forecasting technique can place the most emphasis on recent values? how does it do this? Exponential smoothingweighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1.0).