Journal of Statistics: Advances in Theory and Applications[1] A. J. Conejo, M. A. Plazas, R. Espinola and A. B. Molina,
Day-ahead electricity price forecasting using the wavelet transform
and ARIMA models, IEEE Transactions on Power Systems 20(2) (2005),
1035-1042.
DOI: https://doi.org/10.1109/TPWRS.2005.846054
[2] M. Cottrell, B. Girard, Y. Girard, M. Mangeas and C. Muller,
Neural modeling for time series: A statistical stepwise method for
weight elimination, IEEE Transactions on Neural Networks 6(6) (1995),
1355-1364.
DOI: https://doi.org/10.1109/72.471372
[3] A. K. Dhamija and V. K. Bhalla, Financial time series forecasting:
Comparison of neural networks and ARCH models, International Research
Journal of Finance and Economics 49 (2010), 194-212.
[4] E. Guresen, G. Kayakutlu and T. U. Daim, Using artificial neural
network models in stock market index prediction, Expert Systems with
Applications 38(8) (2011), 10389-10397.
DOI: https://doi.org/10.1016/j.eswa.2011.02.068
[5] B. Hassibi and D. G. Stork, Second order for network pruning:
Optimal brain surgeon, Advances in Neural Information Processing
Systems 5 (1994), 164-171.
[6] R. J. Hyndman and Y. Khandakar, Automatic time series forecasting:
The forecast package for R, Journal of Statistical Software 27(3)
(2008), 1-22.
DOI: https://doi.org/10.18637/jss.v027.i03
[7] I. Khandelwal, R. Adhikari and G. Verma, Time series forecasting
using hybrid ARIMA and ANN models based on DWT decomposition, Procedia
Computer Science 48 (2015), 173-179.
DOI: https://doi.org/10.1016/j.procs.2015.04.167
[8] Y. Le Cun, J. S. Denker and S. A. Solla, Optimal brain damage,
Advances Neural Information Process System 2 (1990), 598-605.
[9] S. Makridakis, A. Anderson, R. Carbone, R. Fildes, M. Hibon, R.
Lewandowski, J. Newton, E. Parzen and R. Winkler, The accuracy of
extrapolation (time series) methods: Results of a forecasting
competition, Journal of Forecasting 1(2) (1982), 111-153.
DOI: https://doi.org/10.1002/for.3980010202
[10] W. Pannakkong, S. Sriboonchitta and V.-N. Huynh, An ensemble
model of ARIMA and ANN with restricted Boltzmann machine based on
decomposition of discrete wavelet transform for time series
forecasting, Journal of Systems Science and Systems Engineering 27(5)
(2018), 690-708.
DOI: https://doi.org/10.1007/s11518-018-5390-8
[11] A. Sfetsos, A comparison of various forecasting techniques
applied to mean hourly wind speed time series, Renewable Energy 21(1)
(2000), 23-35.
DOI: https://doi.org/10.1016/S0960-1481(99)00125-1
[12] B. L. Smith and M. J. Demetsky, Traffic flow forecasting:
Comparison of modeling approaches, Journal of Transportation
Engineering 123(4) (1997), 261-266.
DOI: https://doi.org/10.1061/(ASCE)0733-947X(1997)123:4(261)
[13] Z. Tang and P. A. Fishwick, Feedforward neural nets as models for
time series forecasting, ORSA Journal on Computing 5(4) (1993),
374-385.
DOI: https://doi.org/10.1287/ijoc.5.4.374
[14] L. Wang, H. Zou, J. Su, L. Li and S. Chaudhry, An ARIMA-ANN
hybrid model for time series forecasting, Systems Research and
Behavioral Science 30(3) (2013), 244-259.
DOI: https://doi.org/10.1002/sres.2179
[15] G. P. Zhang, B. E. Patuwo and M. Y. Hu, A simulation study of
artificial neural networks for nonlinear time-series forecasting,
Computers & Operations Research 28(4) (2001), 381-396.
DOI: https://doi.org/10.1016/S0305-0548(99)00123-9
[16] P. G. Zhang, Time series forecasting using a hybrid ARIMA and
neural network model, Neurocomputing 50 (2003), 159-175.
DOI: https://doi.org/10.1016/S0925-2312(01)00702-0
