經由研究一個輸入序列的轉換模式來關注結構性變化之偵測與處理。 / Time series data are often subject to uncontrolled or unexpected interventions, from which various types of outlying observations or structure changes are produced. In this article, we focus on detecting and treating structure change events in multiple time series by studying transfer function models with one input series. Monte Carlo simulations will be used to study the performance of the proposed procedures.