New autoregression criterion adapts to data, outperforming traditional model selection methods.
A new method for choosing the right order for autoregressive models in time series data has been developed. This method combines the strengths of two existing techniques, making it more reliable and flexible. It can adapt to different situations, being consistent when the true order is known and efficient when the order is high. The new criterion outperforms traditional methods when applied to various datasets, showing its adaptability and effectiveness in real-world scenarios.
