PACF is crucial in catalysis research for several reasons: - Identifying Lag Influence: It helps in identifying how past values (lags) of a time series influence current values, which is essential for understanding reaction dynamics. - Model Refinement: By understanding the partial correlations, researchers can refine reaction mechanisms and improve the accuracy of predictive models. - Noise Reduction: PACF aids in distinguishing between actual data trends and random noise, leading to more robust conclusions.
