References
- Chandra, R. 2015. Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction. IEEE Transactions on Neural Networks & Learning Systems, 26(12): 3123. DOI: 10.1109/TNNLS.2015.2404823
- Chen, C, Twycross, J and Garibaldi, J. 2017. A new accuracy measure based on bounded relative error for time series forecasting. Plos One, 12(3): 1–23. DOI: 10.1371/journal.pone.0174202
- Chouikhi, N, Ammar, B, Rokbani, N and Alimi, AM. 2017. PSO-based analysis of Echo State Network parameters for time series forecasting. Applied Soft Computing, 55: 211–225. DOI: 10.1016/j.asoc.2017.01.049
- Egrioglu, E, Yolcu, U, Aladag, CH and Bas, E. 2015. Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Neural Processing Letters, 41(2): 249–258. DOI: 10.1007/s11063-014-9342-0
- Han, M and Mu, DY. 2011. LM algorithm in echo state network for chaotic time series prediction. Control & Decision, 26(10): 1469–1472.
- Huang, MW, Chen, CW, Lin, WC, Ke, SW and Tsai, CF. 2017. SVM and SVM ensembles in breast cancer prediction. Plos One, 12(1):
e0161501 . DOI: 10.1371/journal.pone.0161501 - Jaramillo, J, Velasquez, JD and Franco, CJ. 2017. Research in financial time series forecasting with SVM: Contributions from literature. IEEE Latin America Transactions, 15(1): 145–153. DOI: 10.1109/TLA.2017.7827918
- Jiang, P, Dong, Q, Li, P and Lian, L. 2017. A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction. Applied Soft Computing, 55: 44–62. DOI: 10.1016/j.asoc.2017.01.043
- Li, D, Han, M, Wang, J. 2012. Chaotic time series prediction based on a novel robust echo state network. IEEE Trans Neural Netw Learn Syst, 23(5): 787–799. DOI: 10.1109/TNNLS.2012.2188414
- Liang, Y, Qiu, L, Zhu, J and Pan, J. 2017. A Digester Temperature Prediction Model Based on the Elman Neural Network. Applied Engineering in Agriculture, 33(2): 142–148. DOI: 10.13031/aea.11157
- Liu, C, Hoi, SCH, Zhao, P and Sun, J. 2016.
Online arima algorithms for time series prediction . In: Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, 1867–1873. - Lun, SX, Yao, XS, Qi, HY and Hu, HF. 2015. A novel model of leaky integrator echo state network for time-series prediction. Neurocomputing, 159(1): 58–66. DOI: 10.1016/j.neucom.2015.02.029
- Misaghi, S and Sheijani, OS. 2017. A hybrid model based on support vector regression and modified harmony search algorithm in time series prediction. In: 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 54–60. DOI: 10.1109/CFIS.2017.8003657
- Nieto, PJG, García-Gonzalo, E, Fernández, JRA and Muñiz, CD. 2017. A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain). Journal of Computational & Applied Mathematics, 309(1): 587–602. DOI: 10.1016/j.cam.2016.01.045
- Qiao, J, Li, R, Chai, W and Han, HJ. 2016. Prediction of BOD based on PSO-ESN neural network. Control Engineering, 23(4): 463–467. DOI: 10.15407/fm23.03.463
- Qin, Y, Song, D, Chen, H, Cheng, W, Jiang, G and Cottrell, GJ. 2017. A dual-stage attention-based recurrent neural network for time series prediction. International Joint Conferences on Artificial Intelligence Organization, 2627–2633. DOI: 10.24963/ijcai.2017/366
- Ren, T, Liu, S, Yan, G and Mu, HJ. 2016. Temperature prediction of the molten salt collector tube using BP neural network. IET Renewable Power Generation, 10(2): 212–220. DOI: 10.1049/iet-rpg.2015.0065
- Rezaei, H, Bozorg-Haddad, O and Chu, X. 2018.
Grey Wolf Optimization (GWO) Algorithm . In Advanced Optimization by Nature-Inspired Algorithms. Springer. 81–91. DOI: 10.1007/978-981-10-5221-7_9 - Rojas, I and Pomares, H. 2016. Time Series Analysis and Forecasting. Contributions to Statistics, 43(5): 175–197. DOI: 10.1007/978-3-319-28725-6
- Sacchi, R, Ozturk, MC, Principe, JC and Carneiro, AAFM. 2007. Water Inflow Forecasting using the Echo State Network: a Brazilian Case Study. In: International Joint Conference on Neural Networks. DOI: 10.1109/IJCNN.2007.4371334
- Saremi, S, Mirjalili, SZ, Mirjalili, SM. 2015. Evolutionary population dynamics and grey wolf optimizer. Neural Computing and Applications, 26(5): 1257–1263. DOI: 10.1007/s00521-014-1806-7
- Wen, L, Liang, XM, Long, ZQ, Qin, HY. 2012. RBF neural network time series forecasting based on hybrid evolutionary algorithm. Control & Decision, 27(8): 1265–1268+1272.
- Xiao, Q, Chu, C, Zhao, L. 2017. Time series prediction using dynamic Bayesian network. Optik International Journal for Light and Electron Optics, 135: 98–103. DOI: 10.1016/j.ijleo.2017.01.073
- Yaseen, ZM, Allawi, MF, Yousif, AA, Jaafar, O, Hamzah, FM, El-Shafie, A. 2016. Non-tuned machine learning approach for hydrological time series forecasting. Neural Computing & Applications, 1–13.
- Zhai, J and Cao, J. 2016. The combined prediction model based on time series ARIMA and BP neural network. Statistics and Decision, 3(4): 29–32.
- Zhang, Y, Yu, D, Seltzer, ML and Droppo, J. 2015. Speech recognition with prediction-adaptation-correction recurrent neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 5004–5008. DOI: 10.1109/ICASSP.2015.7178923
- Zhong, S, Xie, X, Lin, L and Wang, F. 2017. Genetic algorithm optimized double-reservoir echo state network for multi-regime time series prediction. Neurocomputing, 238: 191–204. DOI: 10.1016/j.neucom.2017.01.053