Missing data is a common issue in time series analysis. Some strategies to address this include:
Interpolation: Estimating missing values based on surrounding data points. Imputation: Using statistical methods to estimate and fill in missing values. Deletion: Removing incomplete records, though this can reduce the dataset size and statistical power.
