👉 Weighted Temporal Regression (WT) is a statistical method used to analyze and forecast time series data where observations are influenced by their temporal proximity and varying importance over time. Unlike traditional regression models that treat all data points equally, WT assigns weights to each observation based on its distance from the current point and its historical impact or relevance. This approach allows WT to better capture the dynamic changes in relationships between variables over time, making it particularly useful for scenarios where recent data is more indicative of future trends. By incorporating these weights, WT can provide more accurate and timely forecasts, especially in fields like economics, finance, and environmental science, where temporal dynamics play a crucial role.
