由於預測能夠做為解決問題的方案基礎,並且提供參考方向,在日趨複雜的經營環境中,對於企業而言越顯重要,也使得預測在決策過程中扮演著相當重要的角色。點預測為最常使用的預測結果表示方式,然而在生活中有許多事物存在不明確的狀況,而點預測值無法呈現出預測結果的變異,也不足以描述每日或每月的發展趨勢,無法提供決策者更多有用資訊。因此為了處理這些問題,我們使用區間預測來進行本研究。區間預測的優點在於除獲得預測結果的分佈,並量化預測的不確定性,可以給予管理者評估風險並做出決策。根據現有的文獻,資料區間的組成方式並沒有考慮到資料的分佈,如果資料分佈中出現明顯偏斜的結構,將會影響到預測結果的準確程度。因此本研究提出新型的區間組成方法MSL(Mean,Standard deviation and Level),其中的參數會考慮到資料的分佈,可以避免受到資料偏移的影響,可獲得更佳的預測結果。本研究以台灣資訊產品的筆記型電腦銷售額以及美國道瓊工業指數 (Dow Jones Industrial Average Index)做為實證資料,並以逐步迴歸(Stepwise regression, SR)、天真預測法(Naive forecast, NF)、支援向量迴歸(Support vector regression, SVR) 以及結合逐步迴歸與支援向量迴歸(SR-SVR)的混合模式,對新型資料區間組成方式進行預測與評估結果。實驗結果顯示本研究提出的MSL法,能有效性的提高準確度,並且不論哪種區間組合,使用SVR都擁有較好的預測結果,因此顯示對本研究所使用的實證資料而言,使用新型區間組成方式並以SVR為預測技術,較為合適的預測工具。
Forecasting has long been crucial for every company since accurate forecasting results can improve business decision performance. Interval-valued time series forecasting indicates possible future outcomes for upper and lower bounds of interval-valued data and generates a prediction interval. It has the advantage of taking into account the variability and uncertainty so as to reduce the amount of random variation relative to that found in classic point forecasting/ single-valued forecasting model. In this study, a new interval-valued forecasting model based on time-interval information has been proposed. In the proposed forecasting model, a new MSL scheme (mean, standard deviation and level) which is used to describe the bounds of interval-valued data and a support vector regression (SVR) were integrated to construct interval-valued time series forecasting model. A sales data of laptop computer and a Dow Jones stock index data are used as illustrative examples to evaluate the performance of the proposed method. Experimental results showed that the proposed interval-valued time series forecasting model outperforms the naive forecast method, stepwise regression (SR) and integrated SR-SVR method with the new MSL scheme.