應用預測油價於探討校園通勤車輛契約價格之合理性

Study on reasonable pricing of a campus commuter vehicle contract based on forecasts of oil price

王欣桓(Sin-Huan Wang)

指導教授 ： 楊康宏

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2019年)

https://doi.org/10.6840/cycu201900627

摘要

在過去，運用時間序列分析油價之文獻非常多，證明油價對全球之影響極為重要。且本研究要探討契約價格（契約中之浮動價格條件）問題也與油價息息相關，想要了解浮動價格條件之合理性，就必須預測油價。因此，本論文使用台灣中油股份有限公司網站公開之歷史油價進行預測。運用時間序列分析中的自我迴歸移動平均整合模式（Autoregressive Integrated Moving Average method,，簡稱ARIMA）方法進行分析及預測。使用平均絕對值誤差率（Mean Absolute Percentage Error，簡稱MAPE）與平均方根誤差（Root Mean Square Error，RMSE）作為預測結果之績效衡量指標。實證結果為ARIMA（2,1,3）為最佳預測模式。本研究根據模式ARIMA（2,1,3）進行油價預測，並利用油價預測的結果分析原始浮動價格條件之合理性。

關鍵字

契約價格 ； 能源預測 ； 時間序列分析 ； ARIMA模式

並列摘要

In the past, there has been a lot of literature on the use of time series to analyze oil prices, proving the importance of the global impact of oil prices. And this study to explore the contract price problem is also closely related to oil prices（Floating price conditions in a contract）, in order to understand the rationality of floating price conditions, we must predict oil prices. Therefore, this paper uses the historical oil price published on the website of Taiwan China Oil Co., Ltd. to make predictions. Analysis and prediction using Autoregressive Integrated Moving Average method （ARIMA）in time series analysis. Use the Mean Absolute Percentage Error（MAPE）and Root Mean Square Error（RMSE）as the performance measurement pointer for the forecast results. The result is that ARIMA（2,1,3）is the best prediction mode. This study makes oil price prediction according to the model ARIMA（2,1,3）and analyzes the rationality of the original floating price condition.

並列關鍵字

Energy forecast ； Time series analysis ； ARIMA mode ； Contract price

參考文獻

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