臺灣博碩士論文加值系統

Chinese Abstract i
English Abstract ii
Contents iii
List of Figures v
List of Tables vi
1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Aims and Challenges 2
1.3 Contributions 3
1.4 Thesis Organization 4
2 Related Works 5
2.1 Time Series Forecasting 5
2.2 Mixed-frequency Time Series Analysis 6
2.3 High-dimensional Time Series Problem 7
2.4 Commodity Price Forecasting 8
3 Proposed Method 9
3.1 Problem Definition 9
3.2 Proposed Framework 10
3.3 Mixed-Frequency Time Series Alignment 10
3.4 High-Dimensional Time Series Forecasting 12
3.5 Decomposition and Auto-regressive on Target Series 14
3.6 Fusion of Different Outputs 15
4 Experimental Evaluations 16
4.1 Dataset 16
4.2 Experimental Settings 19
4.2.1 Environments 19
4.2.2 Base Time Series Models 19
4.2.3 Evaluation Metrics 21
4.2.4 Roadmap of Experiments 22
4.3 Experimental Results 23
4.3.1 External Experiments 23
4.3.2 Internal Experiments 27
5 Conclusions and Future Work 35
5.1 Conclusions 35
5.2 Future Work 36
References 37

[1] Zakaria Alameer, Ahmed Fathalla, Kenli Li, Haiwang Ye, and Zhang Jianhua. “Multistep-ahead forecasting of coal prices using a hybrid deep learning model” In: Resources Policy, 65 (2020), p. 101588.
[2] Elena Angelini, Jérôme Henry, and Massimiliano Marcellino. “Interpolation and backdating with a large information set”. In: Journal of Economic Dynamics and Control 30.12 (2006), pp. 2693-2724.
[3] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. “Neural machine translation by jointly learning to align and translate”. In: Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015.
[4] Jacob Benesty, Jingdong Chen, Yiteng Huang and Israel Cohen. “Pearson correlation coefficient”. In: Noise reduction in speech processing. (2009), pp. 1-4.
[5] Donald J. Berndt, and James Clifford. “Using dynamic time warping to find patterns in time series”. In: KDD workshop 10.16 (1994), pp. 359-370.
[6] George EP Box, and David A Pierce. “Distribution of residual autocorrelations in autoregressive-integrated moving average time series models”. In: Journal of the American Statistical Association 65.332 (1970), pp. 1509–1526.
[7] Luc Bauwens, Sébastien Laurent, and Jeroen V. K. Rombouts. “Multivariate GARCH models: a survey”. In: Journal of Applied Econometrics 21.1 (2006), pp. 79-109.
[8] Alberto Baffigi, Roberto Golinelli, and Giuseppe Parigi. “Bridge models to forecast the euro area GDP”. In: International Journal of forecasting 20.3 (2004), pp. 447-460.
[9] Engu Chen, and Xin James He. “Crude oil price prediction with decision tree based regression approach”. In: Journal of International Technology and Information Management 27.4 (2019), pp. 2-16.
[10] Hector Climente-Gonzalez, Chloe-Agathe Azencott, Samuel Kaski, and Makoto Yamada. “Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data”. In: Bioinformatics 35.14 (2019), pp. 427-435.
[11] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. “Empirical evaluation of gated recurrent neural networks on sequence modeling”. In: arXiv preprint arXiv:1412.3555. 2014.
[12] Konstantin Dragomiretskiy, and Dominique Zosso. “Variational mode decomposition”. In: IEEE Transactions on Signal Processing 62.3 (2013), pp. 531-544.
[13] Chenyou Fan , Yuze Zhang , Yi Pan , Xiaoyue Li , Chi Zhang , Rong Yuan , Di Wu and Wensheng Wang. “Multi-horizon time series forecasting with temporal attention learning”. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. (2019), pp. 2527-2535.
[14] Claudia Foroni, Massimiliano Marcellino, and Christian Schumacher. “Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials”. In: Journal of the Royal Statistical Society: Series A (Statistics in Society) 178.1 (2015), pp. 57-82.
[15] Eric Ghysels , Pedro Santa-Clara, and Rossen Valkanov. “The MIDAS touch: Mixed data sampling regression models”. In: Discussion paper UNC and UCLA. 2004.
[16] Agathe Girard, Carl Edward Rasmussen, Joaquin Quinonero Candela, and Roderick Murray-Smith. “Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting”. In: Advances in Neural Information Processing Systems. (2003), pp. 545–552.
[17] Norden E Huang, Zheng Shen, Steven R Long, and Manli C Wu. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”. In: Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences 454.1971 (1998), pp. 903-995.
[18] Wolfgang Härdle and Philippe Vieu. “Kernel regression smoothing of time series”. In: Journal of Time Series Analysis 13.3 (1992), pp. 209–232.
[19] Sepp Hochreiter, and Jurgen Schmidhuber. “Long short-term memory”. In: Neural Computation 9.8 (1997), pp. 1735-1780.
[20] Z. Ismail, A. Yahya and A. Shabri. “Forecasting gold prices using multiple linear regression method”. In: American Journal of Applied Sciences 6.8 (2009), pp. 1509-1514.
[21] Guokun Lai, Wei-Cheng Chang, Yiming Yang and Hanxiao Liu. “Modeling long-and short-term temporal patterns with deep neural networks”. In: Proceedings of the 41st international ACM SIGIR Conference on Research & Development in Information Retrieval. ACM. 2018, pp. 95-104.
[22] Si Woon Lee, and Ha Young Kim. “Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation”. In: Expert Systems with Applications 161 (2020), p. 113704.
[23] Yishun Liu, Chunhua Yang, Keke Huang, and Weihua Gui. “Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network”.In: Knowledge-Based Systems 188 (2020), p. 105006.
[24] Lobna Nassar, Ifeanyi Emmanuel Okwuchi, Muhammad Saad, Fakhri Karray, and Kumaraswamy Ponnambalam. “Deep learning based approach for fresh produce market price prediction”. In: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). 2020, pp. 1-7.
[25] Rangsan Nochai1, and Titida Nochai. “ARIMA model for forecasting oil palm price”. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. 2006, pp. 13-15.
[26] Yao Qin, Dongjin Song, Haifeng Cheng, Wei Cheng, Guofei Jiang, and Garrison W Cottrell. “A dual-stage attention-based recurrent neural network for time series prediction”. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press. 2017, pp. 2627–2633.
[27] Frank Schorfheide, and Dongho Song. “Real-time forecasting with a mixed-frequency VAR”. In: Journal of Business & Economic Statistics 33.3 (2015), pp. 366-380.
[28] Rajat Sen, Hsiang-Fu Yu, and Inderjit Dhillon. “Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting”. In: Advances in neural information processing systems 32 (2019).
[29] Byeongchan Seong. “Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models”. In: Economic Modelling 91 (2020), pp. 463-468.
[30] Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu. “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019”. In: Applied Soft Computing 90 (2020), p. 106181.
[31] Siddhaling Urolaginab, Nikhil Sharmaa, Tapan Kumar Dattaa. “A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting”. In: Energy 231 (2021), p. 120963.
[32] Stef Van Buuren, and Karin Groothuis-Oudshoorn. “mice: Multivariate imputation by chained equations in R”. In: Journal of Statistical Software 45.3 (2011), pp. 1-67.
[33] Jue Wang, and Xiang Li. “A combined neural network model for commodity price forecasting with SSA”. In: Soft Computing 22.16 (2018), pp. 5323-5333.
[34] Peng Wu, Hong Yu, Feng Hu, and Yongfang Xie. “A temporal-attribute attention neural network for mixed frequency data forecasting”. In: International Journal of Machine Learning and Cybernetics 13 (2022), pp. 2519-2531.
[35] Tao Xiong, Chongguang Li, and Yukun Bao. “Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China”. In: Neurocomputing 275 (2018), pp. 2831-2844.
[36] Qif Xu, Xingxuan Zhuo, Cuixia Jiang, and Yezheng Liu. “An artificial neural network for mixed frequency data”. In: Expert Systems with Applications 118 (2019), pp. 127-139.
[37] Xiaoxiang Xu, and Mingqiu Liao. “Prediction of Carbon Emissions in China’s Power Industry Based on the Mixed-Data Sampling (MIDAS) Regression Model”. In: Atmosphere 13.3 (2022), p. 423.
[38] Rui Ye, and Qun Dai. “Implementing transfer learning across different datasets for time series forecasting”. In: Pattern Recognition 109 (2021), p. 107617.
[39] Hsiang-Fu Yu, Nikhil Rao, and Inderjit S. Dhillon. “Temporal regularized matrix factorization for high-dimensional time series prediction”. In: Advances in Neural Information Processing Systems 29 (2016).
[40] Lean Yu, Shouyang Wang, and Kin Keung Lai. “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm”. In: Energy Economics 30.5 (2008), pp. 2623-2635.
[41] Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P Xing, and Masashi Sugiyama. “High-dimensional feature selection by feature-wise kernelized lasso”. In: Neural Computation 26.1 (2014), pp. 185–207.
[42] Dabin Zhang , Shanying Chen , Liwen Ling , and Qiang Xia. “Forecasting agricultural commodity prices using model selection framework with time series features and forecast horizons”. In: IEEE Access 8 (2020), pp. 28197-28209.
[43] Jianye Zhang, and Peng Yin. “Multivariate time series missing data imputation using recurrent denoising autoencoder”. In: Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2019, pp. 760-764.
[44] Pinyi Zhang, and Bicong Ci. “Deep belief network for gold price forecasting”. In: Resources Policy 69 (2020), p. 101806.
[45] Yue-Jun Zhang, and Jin-Li Wang. “Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models”. In: Energy Economics 78 (2019), pp. 192-201.
[46] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. “Informer: Beyond efficient transformer for long sequence time-series forecasting”. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2021, pp. 11106-11115.
[47] Xian Zhou, Yanyan Shen , Yanmin Zhu, and Linpeng Huang. “Predicting multi-step citywide passenger demands using attention-based neural networks”. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 2018, pp. 736-744.
[48] Eric Zivot, and Jiahui Wang. “Vector autoregressive models for multivariate time series”. In: Modeling Financial Time Series with S-Plus® (2006), pp. 385–429.