Identifying the optimal lag involves analyzing how past values influence the current data. The autocorrelation function (ACF) and partial autocorrelation function (PACF) plots are common tools for this purpose. ACF shows correlations for different lags, while PACF isolates the impact of each lag. Significant spikes in these plots indicate potential lags to include in your model. Statistical techniques such as the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) can further refine lag selection. By comparing models with different lag structures, you can choose the one with the lowest AIC or BIC value, indicating a good balance between complexity and performance. Cross-validation is another useful approach. Divide your dataset into training and testing subsets, fit models with varying lags, and evaluate their performance using metrics like mean squared error (MSE). Modern libraries like statsmodels or pmdarima offer functions to automate lag selection and testing, making this process more efficient.
How do you identify the optimal lag for a time series model?
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