時間序列預測研究──使用特徵選取、複數模糊模型和GD-WOA-RLSE機器學習方法

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姓名

吳宜庭(Yi-Ting Wu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

時間序列預測研究──使用特徵選取、複數模糊模型和GD-WOA-RLSE機器學習方法
(Research on Time Series Prediction── An Approach Using Feature Selection, Complex Fuzzy, and GD-WOA-RLSE Machine Learning)

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至系統瀏覽論文 (2025-7-1以後開放)

摘要(中)

時間序列分析與機器學習是當今重要議題，隨著大數據時代來臨，資料與特徵維度的增加使得模型在運算更具挑戰性。因此，本次研究將會專注在如何以有限資源減緩資料維度與參數量對模型的風險，提出了一種新的全複數模糊模型（SCFM），利用複數型態結合輸入維度來達到降維的目的，進而減少模型計算複雜度與計算成本，其中，以投影式球形複數模糊集的複數輸出達到雙目標預測。其次，本研究基於傳遞熵的概念改良多目標特徵選擇演算法，為模型提供更有力之特徵，透過輸入空間最適區塊挑選法（GSIS）挑選可用區塊降低模型參數與規則數量，並於機器學習中提出一個新的混合式演算法，稱作GD-WOA-RLE，混和兩種演算法，以高斯鯨群演算法（GD-WOA）更新模型前鑑部參數及以遞迴最小平方估計法（RLSE）更新後鑑部參數。本次研究也透過三個實驗來驗證模型效能，包含兩種單目標預測：以函數逼近檢驗GSIS效能、透過預測台灣積體電路製造（TSMC）的股價資訊驗證所提出之特徵選取的有效性與優勢，最後同時預測貨幣市場中USD2EUR與CNY2EUR的匯率。從實驗結果，驗證了SCFM配合GD-WOA-RLSE在單、雙目標的預測上均有不錯的效能，並且能夠透過減少參數與模型大小使得模型有更好的表現。

摘要(英)

Time series analysis and machine learning are currently important issue. With the rise of the big data, the increase in the number of data and feature dimensions presents challenges in model computation. Therefore, this study focuses on mitigating the risks of high input dimensionality and parameter quantity on models with limited resources. We propose a novel approach called the Sphere Complex Fuzzy Model (SCFM), which combines complex-valued inputs to achieve dimensionality reduction to reduce model computational complexity and costs. The SCFM incorporates the use of Sphere Complex Fuzzy Sets with Projection as the fuzzy sets for dual-target prediction. Furthermore, we enhance the multi-target feature selection algorithm based on transfer entropy, providing more powerful features for the model. We introduce the grid-type selection of input space (GSIS) to select suitable blocks within the input space, reducing the number of model parameters and rules. Additionally, we propose a hybrid algorithm, GD-WOA-RLE, which combines the gaussian distribution-based whale optimization algorithm (GD-WOA) for updating the parameters of If-parts and the recursive least squares estimator (RLSE) for updating the then-parts parameters. We conduct three experiments to evaluate the performance of model, including function approximation using GSIS, predicting stock prices of TSMC, and forecasting exchange rates of USD2EUR and CNY2EUR. The experimental results demonstrate the effectiveness and advantages of the proposed feature selection and the performance of the SCFM with GD-WOA-RLE in single- and dual-target predictions. By reducing parameters and model size, the proposed approach achieves improved model performance.

關鍵字(中)

★ 傳遞熵
★ 特徵選擇
★ 複數模糊系統
★ 球型複數模糊集
★ 複數減法分群法
★ 機器學習
★ 雙目標預測

關鍵字(英)

★ Transfer entropy
★ feature selection
★ complex fuzzy systems
★ sphere complex fuzzy sets
★ subtractive clustering for complex numbers
★ machine learning
★ dual-target prediction

論文目次

摘要 i
Abstract ii
誌謝 iii
目次 iv
圖目次 vi
表目次 vii
記號說明表 ix
專有名詞縮寫表 xi
第一章緒論 1
1.1. 研究動機與背景 1
1.2. 研究方法概述 2
1.3. 論文架構 3
第二章文獻探討 5
2.1. 特徵選取 5
2.2. 資訊理論 6
2.2.1. 資訊熵與互資訊 6
2.2.2. 相對熵與互資訊 7
2.3. 傳遞熵 8
2.3.1. 馬可夫過程 8
2.3.2. 傳遞熵 9
2.3.3. 傳遞熵計算 10
2.4. 複數模糊集 14
2.5. 最佳化演算法 14
2.5.1. 高斯型鯨群演算法 14
2.5.2. 遞迴最小平方估計法 17

第三章研究方法 18
3.1. 多目標特徵選取 18
3.1.1. 影響資訊與傳遞熵 18
3.1.2. 多目標特徵選取演算法 18
3.2. 結構學習 21
3.2.1. 複數型減法分群法 22
3.2.2. 輸入空間最適區塊挑選 24
3.2.3. 投影式球型複數模糊集 26
3.3. 模型設計 28
3.4. GD-WOA-RLSE機器學習方法 29
第四章實驗 32
4.1. 實驗1 32
4.2. 實驗2 38
4.2.1. 實驗2（a） 39
4.2.2. 實驗2（b） 42
4.3. 實驗3 45
第五章實驗結果與討論 51
5.1. 全複數模型之應用：單目標與雙目標預測 51
5.2. 全複數模型之應用：問題複雜性討論 52
5.3. 驗證輸入空間最適區塊挑選的有效性 52
5.4. 驗證基於傳遞熵的特徵選取之有效性與優勢 52
5.4.1. 特徵選取有效性驗證 53
5.4.2. 傳遞熵特徵選取之優勢 53
第六章結論與未來方向 54
參考文獻 56

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指導教授

李俊賢(Chunshien Li)

審核日期

2023-7-12

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