在這篇論文中，我們提出一個基於局部模型的方法，並應用於時間序列預測，時間序列預測需要根據過去的變化趨勢預測未來的發展。我們的方法包含以下數個步驟。首先利用相似度計算方法，找出k個和Query相似的鄰居，這邊的Query指的是根據當下時間點的資料所組成的一個向量，接下來我們需要做延遲係數的挑選，就跟特徵選取一樣，部分延遲係數對我們預測並沒有太大的幫助，且增加我們的運算複雜度，本論文利用互信息找出一組適當的延遲係數集合，互信息是一種可用來量測兩個變數之間的依賴性。最小平方支援向量機是機器學習的一種策略，根據訓練樣本來訓練迴歸模型的參數，這邊的訓練樣本即是我們第一步找出的k個鄰居，除此之外，本論文參考Instance-based learning修改支援向量機的目標函數，讓每一筆訓練樣本基於之前計算出來的距離擁有不一樣的權重，進而發展出一套權重最小平方支援向量機模型，最後利用這個訓練好的模型完成預測。另外本論文的方法也應用於時間序列的多步預測以及多變數時間序列預測。在實驗中，我們提出的方法應用在五個真實世界的資料上，這些資料集包含多變數時間序列，從實驗結果也可以驗證本論文方法的準確度比其他現存的方法更高。

Time series arise frequently when monitoring industrial processes or tracking corporate business metrics. Forecasting time series data is important because it often provides the foundation for decision makings. Statistical methods have been extensively adopted in the forecasting community for the past decades. Recently, machine learning techniques have drawn attention and have helped establish forecasting methods which are serious contenders to the classical statistical counterparts. In this paper, we propose a local modeling approach, based on machine learning, for multivariate time series forecasting. Given a query, a subset of nearest neighbors of the given query are located in the historical data. Proper lags associated with relevant variables for forecasting are determined. A weighted SVM is applied to derive a forecasting model, which can then be used to forecast for the query. The proposed approach has several advantages. It can provide dynamic and adaptive models. It can do both univariate and multivariate time series forecasting. Furthermore, it allows one-step as well as multi-step forecasting. A number of experiments are conducted and the results show the effectiveness of the proposed approach.

誌謝+i
中文摘要+ii
ABSTRACT+iii
目錄+iv
圖目錄+vi
表目錄+viii
簡介+1
研究背景+1
問題描述+2
論文架構+2
文獻探討+4
時間序列+4
直接法+4
遞迴法+4
預測模型+4
多元迴歸分析+5
加權多元迴歸分析+6
自動迴歸移動平均+6
類神經網路+7
支援向量機+8
局部架構+9
研究方法+11
尋找相似鄰居+13
延遲係數選取+15
模型建立+20
訓練階段和測試階段+23
實際範例+24
實驗結果與分析+27
單步預測+27
波蘭電力負載資料集+27
雷射資料集+30
太陽黑子資料集+34
加權股價指數資料集+35
多步預測+39
雷射資料集+39
EUNITE資料集+43
討論+46
結論+50
參考文獻+51

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