現今，減少員工離職率是各大公司的一大課題。如果能預測員工未來是否會 離開公司，將可以幫助公司提前做出反應，有效的挽留人才。不同於過去分析員 工基本信息的相關研究，本篇研究著重於員工出缺勤資料的研究，透過該資料觀 察員工出缺勤行為，達到識別員工離職的可能性。因此我們提出一個神經網路模 型，稱為 SAB 模型，該模型可以擷取員工每個月出缺勤趨勢作為特徵來進行員工 離職預測，以及我們強化 SAB 模型，加入考慮員工過去幾個月的出缺勤行為變化 的功能，稱為 LAB 模型。我們使用現實中的資料集做了大量的實驗，以現實中的 資料集驗證了大多數情況，LAB 模型可以顯著的優於過去提出的方法。

Nowadays, reducing the employee turnover rate is a significant task for companies. If we can predict employees who are going to leave the company in the near future, it will help the company to react in advance and effectively retain talents. Different from most of related works, which analyze the personal information of employees, this study mainly focuses on using the attendance data as the key feature to observe the changes of attendance behavior for identifying the potential of turnover. We propose a neural network model named Short Term Attendance Behavior Model (SAB Model) to extract the monthly attendance trend for predicting the turnover of employees, and further extend it to Long Term Attendance Behavior Model (LAB Model) which takes into account the changes of attendance behavior in recent months. Extensive experiments conducted on a real-world dataset verified that the LAB Model can considerably outperform the state-of-the-art employee turnover prediction works in most cases.

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