財務時間序列資料預測長久以來一直被廣泛地研究探討，因為它是投資者做投資決定的重要依據。然而，這樣的工作有極大的挑戰，因為財務時間序列資料是混雜且不穩固的。過去這方面的研究以類神經網路為主，但是這類方法有著模式解釋力與預測一般化能力不足的問題。
因此本研究的目的在於提出一個混合式方法預測公司股價。本研究提出使用獨立成分分析來瞭解時間序列資料的潛在結構，以及去除資料雜訊，並且以其結果進行分群分析；接著使用支援向量迴歸對每一群資料建立一個預測模式，以冀提升其一般化能力。
本研究進行兩個實驗來驗證所提方法，實驗結果顯示支援向量迴歸有穩健的績效表現。而更重要的是，利用獨立成分分析分群資料建立的支援向量迴歸模式確實比不分群資料建立的支援向量迴歸模式表現較佳。這顯示了我們所提方法可以提升預測模式一般化的能力，也因此驗證了我們所提方法在實際應用上的可行性。

Financial time-series forecasting has long been discussed because of its vitality for making informed investment decisions. This kind of problem, however, is intrinsically challenging due to the data dynamics in nature. Most of the research works in the past focus on artificial neural network (ANN)-based approaches. It has been pointed out that such approaches suffer from explanatory power and generalized prediction ability though.
The objective of this research is thus to propose a hybrid approach for stock price forecasting. Independent component analysis (ICA) is employed to reveal the latent structure of the observed time-series and remove noise and redundancy in the structure. It further assists clustering analysis. Support vector regression (SVR) models are then applied to enhance the generalization ability with separate models built based on the time-series data of companies in each individual cluster.
Two experiments are conducted accordingly. The results show that SVR has robust accuracy performance. More importantly, SVR models with ICA-based clustered data perform better than the single SVR model with all data involved. Our proposed approach does enhance the generalization ability of the forecasting models, which justifies the feasibility of its applications.

審定書 i
誌　謝 ii
摘　要 iii
Abstract iv
Table of contents v
List of figures vi
List of tables vii
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Objective of the research 3
1.3 Organization of the research 4
Chapter 2 Literature Review 5
2.1 Independent Component Analysis 5
2.2 Cluster Analysis 12
2.3 Support Vector Regression 15
Chapter 3 Proposed Approach 19
Step 1 Data Collecting 21
Step 2 Data Preprocessing 21
Step 3 ICA analysis 22
Step 4 Clustering 25
Step 5 Stock Price Predicting 26
Chapter 4 Experiments and Results 28
Chapter 5 Conclusion 51
5.1 Concluding remarks 51
REFERENCES 53

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