In a weighted moving average, what should you do if you believe recent observations are better predictors of the future?

In a weighted moving average, the goal is to enhance the predictive power of the model by assigning different importance to past observations based on how relevant they are deemed to be for forecasting future values. If you believe that recent observations are better predictors of future trends, the appropriate approach is to assign larger weights to these more recent data points.

This prioritization allows the model to respond more sensitively to trends or changes indicated by the latest data, reflecting their greater potential influence on what may happen in the near future. By giving more significance to recent observations, you ensure that the predictions are more aligned with the current situation, which is usually subject to faster changes compared to older data.

Consequently, assigning equal weights to all observations, ignoring past observations entirely, or giving larger weights to distant observations would not support the intention of reflecting the increased relevance of recent data in your analysis. Instead, these alternatives would dilute the impact of the recent trends you consider more predictive, thus compromising the accuracy of your forecast.