Related papers
New model challenges traditional seasonal adjustment methods, revolutionizing time series analysis.
The article explores how seasonal patterns in data can be influenced by other factors like trends and cycles. By developing a new model, researchers found that seasonal and non-seasonal changes can be related, challenging the traditional assumption of no correlation. This new approach allows for better understanding and adjustment of seasonal variations in statistical data.
