What are the best practices for evaluating time series models?

Evaluating time series models effectively requires a focus on temporal structure, robust validation, and domain-specific metrics. Unlike traditional machine learning, time series data has dependencies between observations, so standard practices like random train-test splits can lead to misleading results. Instead, split data chronologically: reserve the most recent period as the test set. For example, if predicting monthly sales, use data up to December 2023 for training and January 2024 onward for testing. Metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) quantify prediction accuracy, while Mean Absolute Percentage Error (MAPE) is useful for relative error. For multi-step forecasts, consider metrics like MASE (Mean Absolute Scaled Error), which compares performance to a naive baseline. These choices ensure evaluations reflect real-world deployment, where models predict future unseen data in sequence.

Validation must account for time dependencies. Walk-forward validation is a common approach: iteratively train on expanding or sliding windows and test on the next time step(s). For instance, if forecasting daily energy demand, train on the first 90 days, predict day 91, then retrain on days 1–91 to predict day 92, and so on. This mimics how models update over time. Additionally, check for overfitting by comparing training and test performance. If a model performs well on training data but poorly on the test set, it may have memorized noise. For complex models like LSTMs, use techniques like dropout or regularization to reduce overfitting. Always report confidence intervals or prediction intervals to communicate uncertainty, especially in applications like financial forecasting where risk assessment matters.

Finally, analyze residuals and model assumptions. Residuals (prediction errors) should resemble white noise—no patterns, trends, or autocorrelation. Plotting residuals over time or using autocorrelation function (ACF) plots can reveal unmodeled seasonality or trends. For example, if residuals spike every 12 months in monthly data, the model may miss annual seasonality. Statistical tests like the Ljung-Box test check for residual autocorrelation. Validate that the model aligns with domain knowledge: a retail demand forecast should reflect known holiday spikes. Compare against baseline models like ARIMA or exponential smoothing to ensure your model adds value. For instance, if a neural network barely outperforms a simple moving average, its complexity may not be justified. These steps ensure the model is both statistically sound and practically useful.