- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 387857, 15 pages
Track Irregularity Time Series Analysis and Trend Forecasting
1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Received 27 August 2012; Revised 27 October 2012; Accepted 27 October 2012
Academic Editor: Wuhong Wang
Copyright © 2012 Jia Chaolong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- D. Julong, The Primary Methods of Grey System Theory, Huazhong University of Science and Technology Press, Wuhan, China, 2005.
- G. Liu and J. Yu, “Gray correlation analysis and prediction models of living refuse generation in Shanghai city,” Waste Management, vol. 27, no. 3, pp. 345–351, 2007.
- M. Marcellino, J. H. Stock, and M. W. Watson, “A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series,” Journal of Econometrics, vol. 135, no. 1-2, pp. 499–526, 2006.
- J. Ding, L. Han, and X. Chen, “Time series AR modeling with missing observations based on the polynomial transformation,” Mathematical and Computer Modelling, vol. 51, no. 5-6, pp. 527–536, 2010.
- R. Kalman, “A new approach to linear filtering and prediction problems,” Transaction of the ASME-Journal of Basic Engineering Series D, vol. 82, pp. 35–45, 1960.
- B. Feil, J. Abonyi, S. Nemeth, and P. Arva, “Monitoring process transitions by Kalman filtering and time-series segmentation,” Computers and Chemical Engineering, vol. 29, no. 6, pp. 1423–1431, 2005.
- R. Kandepu, B. Foss, and L. Imsland, “Applying the unscented Kalman filter for nonlinear state estimation,” Journal of Process Control, vol. 18, no. 7-8, pp. 753–768, 2008.
- D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Explorations in the Microstructure of Cognition, MIT Press, 1986.
- P. P. Balestrassi, E. Popova, A. P. Paiva, and J. W. Marangon Lima, “Design of experiments on neural network's training for nonlinear time series forecasting,” Neurocomputing, vol. 72, no. 4–6, pp. 1160–1178, 2009.
- M. Khashei, M. Bijari, and G. A. Raissi Ardali, “Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs),” Neurocomputing, vol. 72, no. 4–6, pp. 956–967, 2009.
- P. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
- H. Liu, H.-Q. Tian, and Y.-F. Li, “Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction,” Applied Energy, vol. 98, pp. 415–424, 2012.
- P. Areekul, T. Senjyu, H. Toyama, and A. Yona, “A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 524–530, 2010.
- M. Khashei, S. R. Hejazi, and M. Bijari, “A new hybrid artificial neural networks and fuzzy regression model for time series forecasting,” Fuzzy Sets and Systems, vol. 159, no. 7, pp. 769–786, 2008.
- M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479–489, 2010.
- C. H. Aladag, E. Egrioglu, and C. Kadilar, “Forecasting nonlinear time series with a hybrid methodology,” Applied Mathematics Letters, vol. 22, no. 9, pp. 1467–1470, 2009.
- W. Wang, W. Zhang, H. Guo, H. Bubb, and K. Ikeuchi, “A safety-based approaching behavioural model with various driving characteristics,” Transportation Research Part C, vol. 19, no. 6, pp. 1202–1214, 2011.
- W. Wang, Vehicle’s Man-Machine Interaction Safety and Driver Assistance, China Communications Press, Beijing, China, 2012.
- X. Xinping, “Theoretical study and reviews on the computation method of grey interconnet degree,” Systems Engineering-Theory & Practice, vol. 17, no. 8, pp. 77–82, 1997.
- J. H. Stock and M. W. Watson, “Implications of dynamic factor models for VAR analysis,” NBER Working Paper no. 11467, 2005.
- N. L. Yu, D. Y. Yi, and X. Q. Tu, “Analyzing auto-correlation and partial-correlation functions in time series,” Mathematical Theory and Applications, vol. 27, no. 1, pp. 54–57, 2007.
- M. Khayet, C. Cojocaru, and M. Essalhi, “Artificial neural network modeling and response surface methodology of desalination by reverse osmosis,” Journal of Membrane Science, vol. 368, no. 1-2, pp. 202–214, 2011.
- A. Norets, “Estimation of dynamic discrete choice models using artificial neural network approximations,” Econometric Reviews, vol. 31, no. 1, pp. 84–106, 2012.
- C. B. Cai, H. W. Yang, B. Wang, Y. Y. Tao, M. Q. Wen, and L. Xu, “Using near-infrared process analysis to study gas-solid adsorption process as well as its data treatment based on artificial neural network and partial least squares,” Vibrational Spectroscopy, vol. 56, no. 2, pp. 202–209, 2011.