臺灣博碩士論文加值系統

過去研究中，大多將模糊時間序列(Fuzzy Time Series) 應用於商港的貨運量預測或者是在一些相對資料走勢較為平穩的資料預測，尚無研究應用於資料量大且變化量較劇烈的交通資料上。而本研究應用了ARIMA(1,1,1)的模式，將高速公路的車輛偵測器(Vehicle detector)資料輸入ARIMA得到預測值之後再放入模糊時間序列運算預測，發現此種混和的方法較直接將資料輸入模糊時間序列預測的絕對誤差平均值(MAPE)由35.67降至10.64。
可以推論如果將模糊時間序列再修正到適合用來填補車輛偵測器資料，搭配能夠具有過濾器功能之演算法，應可提升模式的精準度且因模糊時間序列之演算邏輯較為單純，處理速度快，可簡化目前演算法填補模式過於複雜之問題，提供給公路管理者參考作為實際可應用之方式。

Past studies, most of the fuzzy time series (Fuzzy Time Series) is applied to the commercial port cargo forecasts or information is more stable relative data to predict the trend in some, there is no research data used in large and dramatic changes in the amount of traffic over the information on. In this study, the application of the ARIMA (1,1,1) model, the highway vehicle detectors (Vehicle detector) data input after the predicted value and then get into the ARIMA time series fuzzy arithmetic prediction, detection methods such mixed more direct data input fuzzy time series forecasting mean absolute error (MAPE) of 35.67 down to 10.64, as can be inferred if the fuzzy time series and then corrected to fit the data used to fill vehicle detectors, with the filter function can have algorithm should be able to improve model accuracy and because of the time series of fuzzy logic algorithms are more simple, fast processing speed, simplify the algorithm to fill the current model is too complex a problem, refer to the highway managers as a way of actual applications.

目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究範圍 2
1.3 研究架構 2
1.4 研究流程 4
第二章 文獻回顧 5
2.1 國內外車輛偵測器技術特性 5
2.1.1 車輛偵測器資料選擇 9
2.2資料探勘回顧 10
2.3演算法填補資料 12
2.4 ARIMA整合移動平均自回歸模型文獻回顧 15
2.5 小結 21
第三章 研究方法 23
3.1研究架構 23
3.2 ARIMA整合移動平均自回歸模型 25
3.2.1 ARIMA 模式的建構 29
3.3 模糊時間序列 30
第四章 研究結果分析 33
4.1各模式績效值比較 41
4.1.1 以ARIMA模式進行即時填補 42
4.1.2 以ARIMA模式混和歷史資料進行即時填補 43
第五章 結論與建議 46
參考文獻 48
附錄 53
 

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