| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系在職專班 | zh_TW |
| DC.creator | 葉復翔 | zh_TW |
| DC.creator | Fu-Hsiang Yeh | en_US |
| dc.date.accessioned | 2019-7-31T07:39:07Z | |
| dc.date.available | 2019-7-31T07:39:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=106552001 | |
| dc.contributor.department | 資訊工程學系在職專班 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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％。 | zh_TW |
| dc.description.abstract | 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. | en_US |
| DC.subject | Deep learning | zh_TW |
| DC.subject | Pirce prediction | zh_TW |
| DC.subject | Financial indices | zh_TW |
| DC.title | GRU-based Coal Price Movement Prediction Using Financial Indices | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |