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題名 適應性學習模型應用於銅價預測
An adaptive learning-based model for copper price forecasting
作者 楊仁瀚
Yang, Ren-Han
貢獻者 林怡伶<br>蔡瑞煌
Lin, Yi-Ling<br>Tsaih, Rua-Huan
楊仁瀚
Yang, Ren-Han
關鍵詞 自適應單隱藏層前饋神經網路
概念飄移
銅價預測
移動窗口
結構性變化
Adaptive single-hidden layer feed-forward neural network
Concept drift
Copper price forecasting
Moving window
Structural change
日期 2022
上傳時間 10-Feb-2022 12:54:02 (UTC+8)
摘要 銅在工業生產過程中扮演著不可或缺的工業原料之一，其價格變動的掌握對於相關的工業計劃與參與者來說至關重要。由於銅價的波動型態經常隨著時間推移而有所變化，往往會造成開發出的預測模型無法有效因應。為了因應銅價的變動特性，在本篇研究中除了檢驗出銅價具有結構性變化的特性並提出適應性學習型預測模型 (ALFM) 在動態變化的環境中學習。因爲結構性轉變在文獻中被證實與概念飄移在本質上存在著相近概念，所以本研究所提出之預測模型中除了加入移動窗口機制來因應銅價背後所存在的概念飄移與結構性轉變，並於自適應單隱藏層前饋神經網路 (ASLFN) 中設計序列型學習 (SS) 機制，以因應類神經網絡在學習具有複雜擬合函數資料時常面臨到梯度消失與擬合過度之問題。
由於 SS 機制是本研究中首次提出，因此其有效性有必要被加以驗證，我們使用長江有色金屬網的銅現貨價進行實驗。實驗結果除了驗證 ALFM 中 SS 機制是有效的之外，即 SS 機制當中的模組安排皆為必要，同時 SS 機制也被證實可以有效解決自適應單隱藏層前饋神經網路所遭遇梯度消失與擬合過度之問題。在所提出的預測模型中移動窗口機制與 SS 機制皆有助於提高預測能力，這使得所提出的 ALFM 比文獻中的其他工具有更好的預測結果，而且訓練時間是可以被接受的。最後，在與文獻中所使用的工具（如：SARIMA、SLFN、SVR、RNN、LSTM 以及 GRU）相比後，可以發現 ALFM 具有更好的預測結果。
An accurate forecasting model for the price volatility of copper plays a vital role in decision-making for industrial projects and related companies. The challenge to deploy models is the change of the data over time, which commonly leads to significant mispredictions. In this paper, the structural change in copper prices has been examined. The adaptive learning-based forecasting model (ALFM) is proposed to learn the patterns under a dynamic changing environment, which combines the moving window mechanism and sequentially structuring (SS) mechanism. The moving window mechanism is used to address the concept drift and structural change behind the copper price. The sequentially structuring (SS) mechanism is designed for the adaptive single hidden layer feed-forward neural network (ASLFN) in response to solving the vanishing gradient and overfitting problems.
The SS mechanism is first proposed in this study and thus should be validated. We use the copper spot prices of Yangtze River (YR) nonferrous metals as application data. The experiment results provide evidence for examining the arrangement of SS mechanism does work in the training process. The proposed ideas of these modules within the SS mechanism can cope with the vanishing gradient or alleviate the overfitting tendency. Furthermore, both the moving window mechanism and SS mechanism in the proposed forecasting model help to improve the prediction ability, which makes the ALFM have better prediction results than other tools in the literature, and the training time is acceptable. The baseline models are seasonal ARIMA model (SARIMA), single-hidden layer feedforward neural network (SLFN), support vector regression (SVR), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU).
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描述 碩士
國立政治大學
資訊管理學系
109356003
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356003
資料類型 thesis
dc.contributor.advisor 林怡伶<br>蔡瑞煌zh_TW
dc.contributor.advisor Lin, Yi-Ling<br>Tsaih, Rua-Huanen_US
dc.contributor.author (Authors) 楊仁瀚zh_TW
dc.contributor.author (Authors) Yang, Ren-Hanen_US
dc.creator (作者) 楊仁瀚zh_TW
dc.creator (作者) Yang, Ren-Hanen_US
dc.date (日期) 2022en_US
dc.date.accessioned 10-Feb-2022 12:54:02 (UTC+8)-
dc.date.available 10-Feb-2022 12:54:02 (UTC+8)-
dc.date.issued (上傳時間) 10-Feb-2022 12:54:02 (UTC+8)-
dc.identifier (Other Identifiers) G0109356003en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138886-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 109356003zh_TW
dc.description.abstract (摘要) 銅在工業生產過程中扮演著不可或缺的工業原料之一，其價格變動的掌握對於相關的工業計劃與參與者來說至關重要。由於銅價的波動型態經常隨著時間推移而有所變化，往往會造成開發出的預測模型無法有效因應。為了因應銅價的變動特性，在本篇研究中除了檢驗出銅價具有結構性變化的特性並提出適應性學習型預測模型 (ALFM) 在動態變化的環境中學習。因爲結構性轉變在文獻中被證實與概念飄移在本質上存在著相近概念，所以本研究所提出之預測模型中除了加入移動窗口機制來因應銅價背後所存在的概念飄移與結構性轉變，並於自適應單隱藏層前饋神經網路 (ASLFN) 中設計序列型學習 (SS) 機制，以因應類神經網絡在學習具有複雜擬合函數資料時常面臨到梯度消失與擬合過度之問題。
由於 SS 機制是本研究中首次提出，因此其有效性有必要被加以驗證，我們使用長江有色金屬網的銅現貨價進行實驗。實驗結果除了驗證 ALFM 中 SS 機制是有效的之外，即 SS 機制當中的模組安排皆為必要，同時 SS 機制也被證實可以有效解決自適應單隱藏層前饋神經網路所遭遇梯度消失與擬合過度之問題。在所提出的預測模型中移動窗口機制與 SS 機制皆有助於提高預測能力，這使得所提出的 ALFM 比文獻中的其他工具有更好的預測結果，而且訓練時間是可以被接受的。最後，在與文獻中所使用的工具（如：SARIMA、SLFN、SVR、RNN、LSTM 以及 GRU）相比後，可以發現 ALFM 具有更好的預測結果。		zh_TW
dc.description.abstract (摘要) An accurate forecasting model for the price volatility of copper plays a vital role in decision-making for industrial projects and related companies. The challenge to deploy models is the change of the data over time, which commonly leads to significant mispredictions. In this paper, the structural change in copper prices has been examined. The adaptive learning-based forecasting model (ALFM) is proposed to learn the patterns under a dynamic changing environment, which combines the moving window mechanism and sequentially structuring (SS) mechanism. The moving window mechanism is used to address the concept drift and structural change behind the copper price. The sequentially structuring (SS) mechanism is designed for the adaptive single hidden layer feed-forward neural network (ASLFN) in response to solving the vanishing gradient and overfitting problems.
The SS mechanism is first proposed in this study and thus should be validated. We use the copper spot prices of Yangtze River (YR) nonferrous metals as application data. The experiment results provide evidence for examining the arrangement of SS mechanism does work in the training process. The proposed ideas of these modules within the SS mechanism can cope with the vanishing gradient or alleviate the overfitting tendency. Furthermore, both the moving window mechanism and SS mechanism in the proposed forecasting model help to improve the prediction ability, which makes the ALFM have better prediction results than other tools in the literature, and the training time is acceptable. The baseline models are seasonal ARIMA model (SARIMA), single-hidden layer feedforward neural network (SLFN), support vector regression (SVR), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU).		en_US
dc.description.tableofcontents Acknowledgement I
摘要 II
Abstract III
Chapter 1 Introduction 1
Chapter 2 Related works 4
2.1 Research on the copper price forecast 4
2.1.1 Traditional statistical methods 4
2.1.2 Learning-based approaches 5
2.1.3 Hybrid models 6
2.1.4 Predictor variables 9
2.2 Structural change and concept drift 11
2.2.1 Structural change of copper prices 11
2.2.2 Concept drift in structural change data 13
2.2.3 Concept drift handling 14
Chapter 3 The proposed adaptive learning-based forecasting model (ALFM) 17
3.1 The moving window mechanism 18
3.2 The sequentially structuring (SS) mechanism 19
Chapter 4 Experimental design 27
4.1 Data description 27
4.2 Structural change test of weekly copper prices of Yangtze River Market 29
4.3 Data preprocessing – the normalization arrangement 31
4.4 Validation and evaluation 31
Chapter 5 Experiment results 34
5.1 SS mechanism validation 34
5.1.1 The learning route 34
5.1.2 Number of adopted hidden nodes 39
5.1.3 Training time 41
5.1.4 Validation for reorganizing module 43
5.1.5 Validation for LTS principle 45
5.1.6 Validation for regularizing module 46
5.2 ALFM evaluation 48
Chapter 6 Discussion and Conclusions 53
6.1 Summary 53
6.2 Theoretical and practical contributions 54
6.3 Limitations and future works 55
References 57
Appendix A - The learning process 72
Appendix B - The number of adopted hidden nodes 76
Appendix C - The training time 77
Appendix D - The number of hidden nodes deleted by reorganizing module 78		zh_TW
dc.format.extent 2483102 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356003en_US
dc.subject (關鍵詞) 自適應單隱藏層前饋神經網路zh_TW
dc.subject (關鍵詞) 概念飄移zh_TW
dc.subject (關鍵詞) 銅價預測zh_TW
dc.subject (關鍵詞) 移動窗口zh_TW
dc.subject (關鍵詞) 結構性變化zh_TW
dc.subject (關鍵詞) Adaptive single-hidden layer feed-forward neural networken_US
dc.subject (關鍵詞) Concept driften_US
dc.subject (關鍵詞) Copper price forecastingen_US
dc.subject (關鍵詞) Moving windowen_US
dc.subject (關鍵詞) Structural changeen_US
dc.title (題名) 適應性學習模型應用於銅價預測zh_TW
dc.title (題名) An adaptive learning-based model for copper price forecastingen_US
dc.type (資料類型) thesisen_US
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dc.identifier.doi (DOI) 10.6814/NCCU202200079en_US