GRU-based Coal Price Movement Prediction Using Financial Indices

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：3.144.124.232

姓名

葉復翔(Fu-Hsiang Yeh) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

(GRU-based Coal Price Movement Prediction Using Financial Indices)

相關論文

★ Dynamic Overlay Construction for Mobile Target Detection in Wireless Sensor Networks	★ 車輛導航的簡易繞路策略
★ 使用傳送端電壓改善定位	★ 利用車輛分類建構車載網路上的虛擬骨幹
★ Why Topology-based Broadcast Algorithms Do Not Work Well in Heterogeneous Wireless Networks?	★ 針對移動性目標物的有效率無線感測網路
★ 適用於無線隨意網路中以關節點為基礎的分散式拓樸控制方法	★ A Review of Existing Web Frameworks
★ 將感測網路切割成貪婪區塊的分散式演算法	★ 無線網路上Range-free的距離測量
★ Inferring Floor Plan from Trajectories	★ An Indoor Collaborative Pedestrian Dead Reckoning System
★ Dynamic Content Adjustment In Mobile Ad Hoc Networks	★ 以影像為基礎的定位系統
★ 大範圍無線感測網路下分散式資料壓縮收集演算法	★ 車用WiFi網路中的碰撞分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

由於市場需求和供應的波動，導致期貨市場的價格漲幅預測非常困難。本文論述了期貨市場中煤炭價格漲幅預測的問題。
該研究使用兩個不同的數據集比較兩個預測模型。第一個數據集包括每日交易數據，而第二個數據集包含每日交易數據和財金指標。2010年至2019年期間來自印度尼西亞和澳大利亞的數據用於實驗。實驗結果表明，第二個模型實現了更高的準確性。市場模擬還表明，第二種模型在一年內的貿易收益大於預算的30％。

摘要(英)

The price movement prediction in the futures market is difficult due to fluctuating demands and supplies. This thesis addresses the problem of coal price movement prediction. The study compares two prediction models using two different datasets. The first dataset includes daily trading data, while the second dataset contains both daily trading data and computed financial indices. The data from Indonesia and Australia between 2010 and 2019 is used for the experiment. The experimental results show that the second model achieves higher accuracy. The market simulation also indicates that the second model enjoys a larger trade gain higher than 30% of
the budget within a year.

關鍵字(中)

★ Deep learning
★ Pirce prediction
★ Financial indices

關鍵字(英)

論文目次

Contents
1 Introduction 1
2 RelatedWork 3
2.1 Traditional machine learning with numerical features 3
2.2 Deep learning with numerical features 4
2.3 Deep learning with graph feature 4
2.4 Natural language processing with numerical features and news features 5
3 Preliminary 7
3.1 Different types of neural networks 7
3.1.1 Artificial Neural Networks 7
3.1.2 Convolutional Neural Networks 8
3.1.3 Recurrent Neural Networks 9
3.1.4 Long Short Term Memory 10
3.1.5 Gated Recurrent Unit 11
3.2 Techniques to reduce overfitting 12
3.2.1 Early stopping 12
3.2.2 Dropou 13
3.2.3 L1 and L2 Regularization 13
3.3 k-fold cross validation 13
4 Design 15
4.1 Data Collection 16
4.2 Data preprocess 17
4.3 Model Construction 21
4.3.1 Data related 21
4.3.2 Model Construction 22
5 Performance 24
5.1 Data Description 24
5.2 Performance Metrics 25
5.2.1 MAE 25
5.2.2 MAPE 25
5.2.3 RMSE 26
5.2.4 Market Simulation 26
5.3 Experimental Results 27
5.3.1 Model Tuning 27
5.3.2 Market Simulation 31
6 Conclusions 33
Reference 34

List of Figures
1 Candlestick representation of numerical time series data. 5
2 ANNs model 8
3 CNNs model 9
4 RNNs model 10
5 LSTM model 11
6 GRU model 12
7 Overview of system architecture 15
8 Overview of system architecture 17
9 The example of three-fold cross validation 23
10 Learning curves of GRU model(Australia) 31
11 Learning curves of GRU model(Indonesia) 31

List of Tables
1 Results of coal price movement prediction 25
2 The MAE of GRU model(Australia) 27
3 The MAE of GRU model(Indonesia) 27
4 The MAPE of GRU model(Australia) 28
5 The MAPE of GRU model(Indonesia) 28
6 The RMSE of GRU model(Australia) 28
7 The RMSE of GRU model(Indonesia) 29
8 The Accuracy of GRU model(Australia) 29
9 The Accuracy of GRU model(Indonesia) 29
10 The training time of GRU model(Australia) 30
11 The training time of GRU model(Indonesia) 30
12 The experimental outcomes with M 1 method 32
13 The experimental outcomes with M 2 method 32

參考文獻

[1]Indexmundi. https://www.indexmundi.com. Accessed: 2019-06-25.
[2]Investing.com. https://www.investing.com. Accessed: 2019-06-25.
[3] Taipower website. https://www.taipower.com.tw/tc/page.aspx?mid=197. Accessed: 2019-06-24.
[4] Yahoo finance. https://finance.yahoo.com. Accessed: 2019-06-24.
[5] Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara. Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pages 1–6. IEEE, 2016.
[6] Sean Bell and Kavita Bala. Learning visual similarity for product design with con- volutional neural networks. ACM Transactions on Graphics (TOG), 34(4):98, 2015.
[7] Anastasia Borovykh, Sander Bohte, and Cornelis W Oosterlee. Conditional time se- ries forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691, 2017.
[8] Jou-Fan Chen, Wei-Lun Chen, Chun-Ping Huang, Szu-Hao Huang, and An-Pin Chen. Financial time-series data analysis using deep convolutional neural networks. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pages 87– 92. IEEE, 2016.
[9] Kai Chen, Yi Zhou, and Fangyan Dai. A lstm-based method for stock returns pre- diction: A case study of china stock market. In 2015 IEEE International Conference on Big Data (Big Data), pages 2823–2824. IEEE, 2015.
[10] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
[11] Luca Di Persio and Oleksandr Honchar. Recurrent neural networks approach to the financial forecast of google assets. International journal of Mathematics and Computers in simulation, 11, 2017.
[12] Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. Deep learning for event-driven stock prediction. In Twenty-Fourth International Joint Conference on Artificial In- telligence, 2015.
[13] Qiyuan Gao. Stock market forecasting using recurrent neural network. PhD thesis, University of Missouri–Columbia, 2016.
[14] Isaac Madan, Shaurya Saluja, and Aojia Zhao. Automated bitcoin trading via machine learning algorithms. URL: http://cs229. stanford. edu/proj2014/Isaac% 20Madan, 20, 2015.
[15] Martina Matta, Ilaria Lunesu, and Michele Marchesi. Bitcoin spread prediction using social and web search media. In UMAP Workshops, pages 1–10, 2015.
[16] Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. Convolutional neural networks over tree structures for programming language processing. In Thirtieth AAAI Con- ference on Artificial Intelligence, 2016.
[17] Jigar Patel, Sahil Shah, Priyank Thakkar, and K Kotecha. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1):259–268, 2015.
[18] Akhter Mohiuddin Rather, Arun Agarwal, and VN Sastry. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6):3234–3241, 2015.
[19] Abhishek Sehgal and Nasser Kehtarnavaz. A convolutional neural network smart- phone app for real-time voice activity detection. IEEE Access, 6:9017–9026, 2018.
[20] Devavrat Shah and Kang Zhang. Bayesian regression and bitcoin. In 2014 52nd annual Allerton conference on communication, control, and computing (Allerton), pages 409–414. IEEE, 2014.
[21] Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. Forecasting stock prices from the limit order book using convolutional neural networks. In 2017 IEEE 19th Conference on Business Informatics (CBI), volume 1, pages 7–12. IEEE, 2017.
[22] Jian Yang, Ruonan Rao, Pei Hong, and Peng Ding. Ensemble model for stock price movement trend prediction on different investing periods. In 2016 12th International Conference on Computational Intelligence and Security (CIS), pages 358–361. IEEE, 2016.
[23] Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. Stock price prediction via discov- ering multi-frequency trading patterns. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 2141–2149. ACM, 2017.

指導教授

孫敏德

審核日期

2019-7-31

推文