Which forecasting model applies uniform weight to past observations?

The moving average model applies uniform weight to past observations by calculating the average of a specific number of previous data points. This model treats all observations within the selected period equally, meaning that each data point contributes equally to the forecast. By doing so, it smooths out short-term fluctuations and highlights longer-term trends, making it useful for time series data where patterns may not be immediately evident.

In contrast, exponential smoothing applies decreasing weights to past observations, placing more emphasis on recent data while gradually reducing the influence of older data. Causal models use relationships between variables to predict outcomes and often depend on multiple influencing factors, thus not applying uniform weight. Subjective models rely on expert judgment or intuition rather than quantitative data, which does not align with the concept of applying uniform weights at all.