What does exponential smoothing use for forecasting?

Exponential smoothing is a forecasting technique that effectively uses a weighted average of past data, with more emphasis placed on the most recent observations. This method adjusts the weights applied to the historical data, meaning that older data points contribute less to the forecast than newer ones.

By focusing on recent data, exponential smoothing responds more quickly to changes in the data trends, making it particularly useful for time series forecasting where patterns may shift over time. This approach is beneficial in many applications because it allows the forecaster to capture trends while also maintaining a degree of stability, as it does not react too dramatically to every single data point.

In contrast, uniform averages disregard the temporal nature of the data, using all past data equally, which may dilute the impact of recent trends. Relying solely on the most recent data point ignores the broader context and can lead to misleading forecasts. Incorporating a random sample of past data points may introduce variability that isn't representative of the ongoing trend, leading to less reliable forecasts. Thus, the correct answer illustrates the fundamental principle of exponential smoothing, emphasizing the importance of recent information in creating more accurate forecasts.