Title: 以時間序列對台灣電腦公司進行商業解析及績效預測
Using Time Series to Conduct Business Analytics and Performance Prediction for Taiwanese Computer Companies
Authors: 鄭浩宇
Cheng, Hao-Yu
Wang, Chih-Hsuan
Keywords: 台灣電腦產業;變數篩選;時間序列;商業解析;績效預測;動態迴歸;Taiwanese computer industry;feature selection;time series;business analytics;performance prediction;dynamic regression
Issue Date: 2017
Abstract: 從電腦問世之後,大家對它的依賴程度與日俱增,但是現階段卻面臨著不少的挑戰。本研究引用1992年宏碁董事長施振榮先生提出微笑曲線的特性運用在電腦產業,依其特性將產業區分為工業電腦、代工電腦與品牌電腦,透過變數篩選後結合時間序列的分析來預測未來的績效每股盈餘。第一階段使用商業智慧當中的資料採礦技術及迴歸方法,分別為分類樹、隨機森林、多元適應性雲形迴歸與複迴歸四種方法,使用平均平方誤差、平均絕對誤差及平均絕對百分差作為評比標準並進行交叉驗證,選擇出四種方法中誤差最低的一種後,進行各子產業的重要變數篩選。並由代表性公司研華、廣達、技嘉作為研究對象,首先透過Granger Test檢定變數是否有時間遞延的存在,分別使用複迴歸分析、多元適應性雲形迴歸與時間序列中的ARIMA與動態迴歸對公司建模,最後根據三種誤差評判的標準,選出最好的模型,然後針對個別公司進行未來一年的績效預測及商業解析。 研究結果除了證明挑選的變數能反映產業特性外,也發現動態迴歸與ARIMA兩種方法建模的誤差優於多雲適應性雲形迴歸及複迴歸,而動態迴歸又略優於ARIMA,因此本研究將根據動態迴歸的模型進行績效預測,提供給企業及投資人參考。
Since the computer was invented, people have relied on it heavily. However, the challenges it faces are more than ever. This study divides the Taiwanese computer industry into three business model: industrial personal computer (IPC), electronic manufacturing service (EMS) and original brand manufacturing (OBM). The first phase is to extract significant performance indicators by Random Forest and identify the causations between predictors and outcomes by Granger Causality Test. Next, using Dynamic regression to incorporate the temporal impacts of leading predictors on lagging outcomes into the process of performance prediction. The results indicate that the variables selected by random forest are representative. Besides, the three indices, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), indicate that each model, dynamic regression in particular, is robust enough. As a result, we believe the forecasting and confidence interval is indeed reliable so that this reference can be an information for executives and investors.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070453339
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