New method detects outliers in time series, improving accuracy and predictions.
The article introduces a method to find and estimate outliers in time series data by using functional autoregressive models. These outliers can be either additive or innovation outliers. The method was tested on both nonlinear and linear time series data, showing that it is effective for various types of data, including those following nonlinear patterns like self-exciting threshold autoregressive models.
