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| Title: | 時間序列預測在匯率及股價混合模型比較分析之研究
A Comparison of Hybrid Models of Time Series Forecasting to Exchange Rate and Stock Index |
| Authors: | 林漢洲
Lin, Han-Chou |
| Contributors: | 楊亨利
Yang, Heng-Li
林漢洲
Lin, Han-Chou |
| Keywords: | 隨機漫步
ARIMA
最小平方支持向量迴歸
極限學習機
粒子群
決策樹
Decision Tree
ARIMA
Least squares support vector regression
Empirical mode decomposition
Extreme learning machine
Particle swarm optimization |
| Date: | 2018 |
| Issue Date: | 2018-08-29 15:47:36 (UTC+8) |
| Abstract: | 本研究整合線性方法ARIMA及非線性方法如最小平方支持向量迴歸 (Least Square Support Vector Regression, LSSVR)、極限學習機 (Extreme Learning Machine, ELM)以及資料處理演技術 經驗模態分解(Empirical Mode Decomposition, EMD)，及最佳化方法 粒子群 (Particle Swarm Optimization, PSO)以建立多個混合預測模型，嘗試在匯率及股價的160狀況下 (條件考量5個匯率集+5個指數集共10個資料集x 5個資料長度10%，30%，50%及100%資料比率，及4種訓練資料與全部資料之比率60%，70%，80%及90%) 找到最佳模型。研究結果顯示考量模型精簡性、預測精確度及取代性，對匯率來說，準則為MAPE時，最佳模型為EMD+LSVR and EMD+ELM，準則為DS時，最佳模型為EMD+LSVR 及 LSSVR。對指數來說，準則為MAPE時，最佳模型為EMD+LSSVR and EMD+ELM，準則為DS時，最佳模型為EMD+ELM 及 ELM。
This study builds several hybrid models by combining linear approach ARIMA and nonlinear approaches, such as least squares support vector regression, extreme learning machine, and data processing technique, empirical mode decomposition, and optimizing models by particle swarm optimization to attempt to find the best fitting models among 160 cases generated with (5 exchange rate and 5 Stock Index) datasets * 4 periods * 4 ratios. Results show that considering average MAPE, for exchange rate, the best model is EMD+LSSVR+ELM+PSO, while considering models fitting to data and models simplification, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data and models simplification as well, the best model is EMD+LSVR and LSSVR. For stock index, the best model is EMD+ELM, while considering models fitting to data, the best model is EMD+LSVR and EMD+ELM. For DS, considering average DS, the best model is EMD+LSSVR, while considering models fitting to data as well, the best model is EMD+ELM and ELM. |
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| Description: | 博士
國立政治大學
資訊管理學系
98356508 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0098356508 |
| Data Type: | thesis |
| DOI: | 10.6814/DIS.NCCU.MIS.028.2018.A05 |
| Appears in Collections: | [資訊管理學系] 學位論文
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