Combining Data-Analytics and Time Series to Forecast Semiconductor Companies of Taiwan

• Main Authors: Chiang, Ying-Hsien, 姜穎憲
• Other Authors: Wang, Chih-Hsuan
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/unxqdh

碩士 === 國立交通大學 === 工業工程與管理系所 === 105 === Taiwanese semiconductor industry has occupied an important position in the world. Total value of out-put of semiconductor of Taiwan follows behind the United States. Number of Taiwanese companies, however, is fewer and fewer because of rising of Chain. From now on, some of companies which have scale are extremely higher than others. Thus, the small and median scale companies would like to figure problems out by data-analytics and KPIs. In other hand, large corporations would like to improve themselves for worldwide market as well. This research has two parts. In the first place, this research would use random forest to find out important variables which highly affected EPS. In addition, this research would use MLR and MARS models to predict leading companies which included MTK, TSMC and SPIL. In the second place, this research would use Granger causality test and unit root test to check if important variables are lagging for EPS and if models are stationary and use ARIMA and VAR models to predict each leading companies and check model fitting by MSE, MAD and MAPE. At last, this research would forecast the future EPS of leading companies by ARIMA and VAR models and evaluate the operation of companies.