Certified Production & Operations Manager (POM) Practice Exam

Which is not a characteristic of simple moving averages applied to time series data?

1. Weights each historical period equally

2. Requires only last period's forecast and actual data

3. Smoothes random variation in the data

4. Smooths real variations in the data

The correct answer is: Requires only last period's forecast and actual data

Simple moving averages are a commonly used method for analyzing time series data. They calculate averages over a specified number of past observations, assigning equal weight to each data point within the selected period. This approach helps in identifying trends by smoothing out fluctuations that occur due to randomness in the data. The characteristic that is not associated with simple moving averages is that they require only the last period's forecast and actual data. Instead, simple moving averages rely on a complete set of data points from previous periods to compute the average. This method considers the entire series of past data points over the predefined time frame rather than just the most recent ones. In contrast, methods like exponential smoothing, where more weight is given to the most recent data, might only need the last period's data to adjust predictions. In summary, the definition of a simple moving average centers on its historical averaging of several time periods rather than focusing solely on the latest observed data, making it distinct from approaches that do not leverage the full historical context.