Which method is best described as using weights ranging from 0 to 1 for data stabilization?

Exponential Smoothing is a forecasting technique that applies weighted averages to past data points, where the weights decrease exponentially for older observations. This means that more recent data is given more significance, while older data contributes less to the forecasted values. The weights used in exponential smoothing range from 0 to 1.

When forecasting, it’s important to prioritize recent trends, as they are often more indicative of future performance. Exponential smoothing effectively captures these trends and seasonality by adjusting the weights accordingly.

In contrast, differencing is typically used in time series analysis to stabilize the mean of a series by removing changes in the level of a time series, not by applying weights. Consensus forecasting focuses on gathering input from various experts to generate forecasts, while econometric forecasting incorporates statistical methods and economic theory to predict future trends without the same emphasis on weight adjustment for stability.