G. Box, Box and Jenkins: Time Series Analysis, Forecasting and Control, pp.161-215, 2013.

J. H. Stock and M. W. Watson, Chapter 10 Forecasting with Many Predictors, Handbook of Economic Forecasting, vol.1, pp.515-554, 2006.

M. Ceci, R. Corizzo, F. Fumarola, D. Malerba, and A. Rashkovska, Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction, IEEE Transactions on Industrial Informatics, vol.13, issue.3, pp.956-966, 2017.

C. D. Dumitru, A. Gligor, and C. Enachescu, Solar Photovoltaic Energy Production Forecast Using Neural Networks, Procedia Technology, vol.22, pp.808-815, 2016.

A. Gandelli, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, Hybrid model analysis and validation for PV energy production forecasting, 2014 International Joint Conference on Neural Networks (IJCNN), pp.1957-1962, 2014.

J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-pison et al., Review of photovoltaic power forecasting, Solar Energy, vol.136, pp.78-111, 2016.

S. Johansen, Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, vol.59, issue.6, pp.1551-1580, 1991.

A. E. Hoerl and R. W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.12, issue.1, pp.55-67, 1970.

T. Zhang, Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms, Proceedings of the Twenty-First International Conference on Machine Learning. ICML '04, p.116, 2004.

L. Bottou, Stochastic Gradient Descent Tricks, Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, pp.421-436, 2012.

C. F. Tsai, Feature selection in bankruptcy prediction, Knowledge-Based Systems, vol.22, issue.2, pp.120-127, 2009.

C. F. Tsai and Y. C. Hsiao, Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches, Decision Support Systems, vol.50, issue.1, pp.258-269, 2010.

X. Zhang, Y. Hu, K. Xie, S. Wang, E. W. Ngai et al., A causal feature selection algorithm for stock prediction modeling, Neurocomputing, vol.142, pp.48-59, 2014.

K. P. Adragni and R. D. Cook, Sufficient dimension reduction and prediction in regression, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.367, pp.4385-4405, 1906.

C. W. Granger, Testing for causality, Journal of Economic Dynamics and Control, vol.2, pp.329-352, 1980.

T. Schreiber, Measuring Information Transfer, Physical Review Letters, vol.85, issue.2, pp.461-464, 2000.

L. Barnett, A. B. Barrett, and A. K. Seth, Granger causality and transfer entropy are equivalent for Gaussian variables, Physical Review Letters, vol.103, issue.23, 2009.

P. Piotr, H. Youssef, C. Alain, and L. Lakhal, Improving multivariate time series forecasting with random walks with restarts on causality graphs, ICDM Workshops, 2017.