You are working on a classification problem with time series data, such as predicting stock prices or classifying events based on historical data. After conducting just a few experiments using random cross-validation, you achieved an Area Under the Receiver Operating Characteristic Curve (AUC ROC) value of 99% on the training data. This high performance was achieved without exploring any sophisticated algorithms or spending time on hyperparameter tuning. Given the nature of your data and the approach taken so far, what should your next step be to correctly address and fix any underlying issues?