Which forecasting model requires little record keeping of past data?

The exponential smoothing forecasting model is designed to require minimal record keeping of past data, making it an efficient choice for either short-term or long-term forecasting needs. This method emphasizes the most recent observations by assigning them a greater weight while applying a decay factor to older data.

Unlike moving averages or weighted moving averages, which necessitate maintaining a larger data set for calculations, exponential smoothing enables forecasters to make predictions based primarily on the latest data point and a smoothing constant, or alpha. This significantly reduces the complexity involved in record keeping, as only the most current forecast and the last period's actual data need to be stored for updates.

The naïve method, while simple, typically relies on the most recent single observation for forecasts, but it does not capture trends or seasonality effectively. Thus, it is less useful for making informed predictions compared to the systematic approach of exponential smoothing. Therefore, the emphasis on minimal record keeping within exponential smoothing aligns with its ability to streamline forecasting processes effectively.