Weighted Moving Averages in Forecasting

Weighted Moving Averages is a time series forecasting technique that assigns different weights to the data points in a moving average calculation. It is a variation of the simple moving average method, where each data point in the moving average is given a specific weight based on its relative importance or significance. This weighting scheme allows for more emphasis to be placed on recent data or specific periods of interest.

The process of calculating a weighted moving average involves the following steps:

  1. Determine the number of periods (n) to include in the moving average. This represents the window size or the number of data points considered for the forecast.

  2. Assign weights to each data point within the window. The weights can be based on a predetermined pattern or based on the relative importance of the data points. The sum of all weights should be equal to 1.

  3. Multiply each data point by its corresponding weight.

  4. Sum the weighted data points to calculate the forecasted value for the next period.

Here’s an example to illustrate the calculation of a weighted moving average:

Let’s say we want to forecast monthly sales for a product based on the last 5 months of data. We assign weights of 0.1, 0.2, 0.3, 0.2, and 0.1 to the most recent month, the second most recent, and so on, respectively. The data for the last 5 months is as follows:

Month 1: 100 units
Month 2: 120 units
Month 3: 150 units
Month 4: 140 units
Month 5: 110 units

Using the weights mentioned above, we calculate the weighted moving average for the next period:

Weighted Moving Average = (0.1 * 110) + (0.2 * 140) + (0.3 * 150) + (0.2 * 120) + (0.1 * 100) = 11 + 28 + 45 + 24 + 10 = 118 units

The forecasted sales for the next period (Month 6) using the weighted moving average method is 118 units.

By assigning different weights to the data points, the weighted moving average method allows for greater flexibility in capturing trends or changes in the data over time. The weights can be adjusted to place more importance on recent data or to reflect specific business considerations.

It’s important to note that the selection of weights requires careful consideration and domain knowledge. Different weighting schemes can be used based on the characteristics of the data and the specific forecasting requirements.

Weighted Moving Averages is just one of many techniques available in quantitative forecasting. It provides a way to smooth out fluctuations in the data while giving more weight to recent observations. This can be useful for forecasting when there is a trend or when recent data is considered more relevant to future predictions.