隨著科技的快速的發展，消費者對於產品的需求愈來愈高，企業也必須不斷追求創新以滿足消費者的需求，使得產品的生命週期愈來愈短暫，在產品銷售量的評估上也愈來愈重要。企業為了獲得更大的利潤便須有效率的安排生產計畫並準確的評估銷售，尤其銷售量會直接反映企業的營利狀況，因此銷售量的預測變成為了企業獲利的重要關鍵之一。
由於在創新科技產品中，消費性電子產品在現今的消費市場佔有重要地位，故本研究將以智慧型手機(iPhone)的銷售量作為研究對象，智慧型手機銷售量通常以季來評估，iPhone每年推出新一代的創新產品，在智慧型手機生命週期愈來愈短的情況下，銷售量的預測就顯得愈來愈重要。而由於Bass擴散模型對於iPhone銷售量之預測有逐漸失準的趨勢，為了使預測結果維持在高準度之水準，本研究以結合Bass擴散模型與自我迴歸整合移動平均(ARIMA)模型進行預測，並比較結合前後之差異及探討。
本研究發現Bass模型的預測能力於前三季的準確度非常高，但其準確度會隨著時間漸漸失準，因此本研究加入時間序列的概念，結合ARIMA模型進行預測後，發現由於考慮了序列值之間的自相關程度，使預測值之準確度有明顯提升。

In recent years, people care about the demand of product so much that companies must improve product quality and pursue innovation constantly to meet consumer’s demand. As a result, the product lifetime of mobile phones has been shortened. In order to make more profits, enterprises should arrange the production plan effectively and evaluate sales volume accurately. Therefore, it’s important that companies modify marketing strategies and manufacturing processes based on consumer behavior.
The purpose of this research is to combine Bass diffusion model and ARIMA model to establish an innovation diffusion model which can be applied to modify the forecast the innovation products. This project will take the global sales of iPhone as research object. The researcher will able to forecasting sales volume accurately. After adding the concept of time series and concerning the auto-regressive among the quarters, the accuracy of predicted value would be better.

中文文獻
陳旭昇(2012)《時間序列分析－總體經濟與財務金融之應用》，東華書局
楊奕農(2005)《時間序列分析－經濟與財務上之應用》，雙葉書廊
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