臺灣博碩士論文加值系統

近年來，傳統的時間序列模型已被廣泛的研究。以前的時間序列方法雖然根據歷史資料預測未來的問題，但卻主觀給定時間間隔的長度。Song 和 Chissom在1993年提出模糊時間序列來解決傳統時間序列方法的問題。到目前為止，許多研究者提出不同的模糊時間序列模型來處理不確定和模糊的資料。過去的研究大多只討論前期和當期的關係來作預測。在股票市場中，股票的價格會受前數期股票價格所影響。再者，以往時間序列模型並沒有考慮模糊關係的權重。基於上述原因，本研究提出一個混合模型，採用自動切割演算法來計算區間的長度，並自動計算上下界。利用關聯規則來建立模糊規則，根據Apriori演算法的概念建立多期的模糊規則。為了驗證所提出的模型，本研究採用黃金價格及匯率(美金VS台幣)作為實驗資料集，研究結果顯示該模型的預測正確率優於所比較之方法。

In the recent years, traditional time series model has been widely researched. The previous time series methods can predict future problems based on historical data, but have a problem that determines subjectively the length of intervals. Song and Chissom (1993) proposed the fuzzy time series to solve the problem of traditional time series methods. So far, many researchers have proposed different fuzzy time series models to deal with uncertain and vague data. Besides, the consideration of a forecasting stage only discusses the relations for previous period and next period. In the financial markets, the price is influenced by the pervious price. For these reasons, this study uses a granular spread partition (GSP) algorithm to calculate the length of intervals under the given number of linguistic value, and automatically create the lower bound and upper bound of universe of discourse. In addition, a shortcoming of previous time series models didn’t consider appropriately the weights of fuzzy relations. This study builds fuzzy rule based on association rules and compute the cardinality of each fuzzy relation. Then, calculating the weights of fuzzy relations solve above problems. Moreover, the proposed method is able to build the high-orders fuzzy rules based on concept of large itemsets of Apriori. To verify the proposed model, the gold price datasets and exchange rates (US Dollar (USD) vs. Taiwan Dollar (TWD)) are employed as experimental datasets. This study compares the forecasting accuracy of proposed model with other methods, and the comparison results show that the proposed method has better performance than other methods.

中 文 摘 要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
1. Introduction 1
1.1 Background 1
1.2 Motivation and Objectives 1
2. Related Work 4
2.1 Fuzzy Sets Theory 4
2.2 Fuzzy Time Series 10
2.3 Association Rule 12
2.4 Adaptive Expectation Method 13
3. Proposed a Novel Fuzzy High-Order Time Series Model 14
3.1 Research Concepts 14
3.2 Algorithm 16
4. Expermental and Comparison 27
4.1 Forecasting for Gold Price 27
4.2 Forecasting for Exchange Rates (USD vs. TWD) 32
5. Profit Evaluations 35
6. Conclusions 37
References 39

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