Measuring General Associations in Time Series: An Adaptation and Empirical Evaluation of the CODEC Coefficient in Determining Autoregressive Dynamics
时间序列一般关联的度量:CODEC 在自回归滞后识别中的改造与评估
Identifying the number of lags to include in an autoregressive model remains an open research problem due to the computational burden of treating it as a hyperparameter, especially in complex models. This study explores model-agnostic association measures, including Pearson, Spearman, and an adaptation of the recently proposed conditional dependence coefficient (CODEC), for guiding lag selection in time series. We adapt and implement the CODEC-based Feature Ordering by…