博碩士論文 106552001 詳細資訊




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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)
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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
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指導教授 孫敏德 審核日期 2019-7-31
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