1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: 2015, TensorFlow: Large-scale machine learning on heterogeneous systems.
2. Adhikari, R., Agrawal, R.: 2013, An Introductory Study on Time Series Modeling and Forecasting. ISBN 978-3-659-33508-2. DOI
.
3. Ahluwalia, H.S.: 1998, The predicted size of cycle 23 based on the inferred three-cycle quasi-periodicity of the planetary index ap. J. Geophys. Res.103(A6), 12103. DOI
.
4. Bai, S., Zico Kolter, J., Koltun, V.: 2018, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv
.
5. Bai, Y., Li, Y., Wang, X., Xie, J., Li, C.: 2016, Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos. Pollut. Res.7(3), 557. DOI
.