Statistical vs. Deep Learning Forecasting: A Comparative Analysis

The article likely discusses the strengths and weaknesses of statistical and deep learning models in forecasting. A good analysis will consider various datasets, model complexities, and evaluation metrics for a comprehensive comparison.

• •Statistical methods often provide interpretable results and are suitable for datasets with clear patterns.
• •Deep learning models excel at handling complex data and identifying non-linear relationships, but require more data.
• •The best approach depends on the specific dataset, desired accuracy, and the trade-off between interpretability and performance.