Time Series Smoothing Improving Forecasting

Romanuke, Vadim

Time Series Smoothing Improving Forecasting

Applied Computer Systems

Volume 26 (2021): Issue 1 (May 2021)

By:

Vadim Romanuke

Open Access

|Jun 2021

References

[1] B. Schelter, M. Winterhalder, and J. Timmer, Eds., Handbook of Time Series Analysis: Recent Theoretical Developments and Applications. Wiley, 2006. https://doi.org/10.1002/978352760997010.1002/9783527609970
Search in Google Scholar Back to article
[2] V. Kotu and B. Deshpande, “Chapter 10 – Time Series Forecasting,” in Predictive Analytics and Data Mining, V. Kotu, B. Deshpande, Eds. Morgan Kaufmann, 2015, pp. 305–327. https://doi.org/10.1016/B978-0-12-801460-8.00010-010.1016/B978-0-12-801460-8.00010-0
Search in Google Scholar Back to article
[3] J. G. De Gooijer and R. J. Hyndman, “25 Years of Time Series Forecasting,” International Journal of Forecasting, vol. 22, iss. 3, pp. 443–473, 2006. https://doi.org/10.1016/j.ijforecast.2006.01.00110.1016/j.ijforecast.2006.01.001
Search in Google Scholar Back to article
[4] C. Villasenor, “Chapter 2 – Hyperellipsoidal Neural Network Trained With Extended Kalman Filter for Forecasting of Time Series,” in Artificial Neural Networks for Engineering Applications, A. Y. Alanis, N. Arana-Daniel, C. Lуpez-Franco, Eds. Academic Press, 2019, pp. 9–19. https://doi.org/10.1016/B978-0-12-818247-5.00011-310.1016/B978-0-12-818247-5.00011-3
Search in Google Scholar Back to article
[5] V. Kotu and B. Deshpande, “Chapter 12 – Time Series Forecasting,” in Data Science (Second Edition), V. Kotu, B. Deshpande, Eds. Morgan Kaufmann, 2019, pp. 395–445. https://doi.org/10.1016/B978-0-12-814761-0.00012-510.1016/B978-0-12-814761-0.00012-5
Search in Google Scholar Back to article
[6] M. Fakhfekh and A. Jeribi, “Volatility Dynamics of Crypto-Currencies’ Returns: Evidence from Asymmetric and Long Memory GARCH Models,” Research in International Business and Finance, vol. 51, 101075, 2020. https://doi.org/10.1016/j.ribaf.2019.10107510.1016/j.ribaf.2019.101075
Search in Google Scholar Back to article
[7] F. C. Palm, “Chapter 7 – GARCH Models of Volatility,” in Handbook of Statistics, vol. 14. Elsevier, 1996, pp. 209–240. https://doi.org/10.1016/S0169-7161(96)14009-810.1016/S0169-7161(96)14009-8
Search in Google Scholar Back to article
[8] E. Ghysels, D. R. Osborn, and P. M. M. Rodrigues, “Chapter 13 – Forecasting Seasonal Time Series,” in Handbook of Economic Forecasting, vol. 1, G. Elliott, C. W. J. Granger, A. Timmermann, Eds. Elsevier, 2006, pp. 659–711. https://doi.org/10.1016/S1574-0706(05)01013-X10.1016/S1574-0706(05)01013-X
Search in Google Scholar Back to article
[9] H. Shimodaira, “Chapter 36 – Time-Series Prediction,” in Expert Systems, C. T. Leondes, Ed. Academic Press, 2002, pp. 1259–1313. https://doi.org/10.1016/B978-012443880-4/50080-610.1016/B978-012443880-4/50080-6
Search in Google Scholar Back to article
[10] R. DiPietro and G. D. Hager, “Chapter 21 – Deep learning: RNNs and LSTM,” in Handbook of Medical Image Computing and Computer Assisted Intervention, S. K. Zhou, D. Rueckert, G. Fichtinger, Eds. Academic Press, 2020, pp. 503–519. https://doi.org/10.1016/B978-0-12-816176-0.00026-010.1016/B978-0-12-816176-0.00026-0
Search in Google Scholar Back to article
[11] T. Masters, “Chapter 4 – Time-Series Prediction,” in Practical Neural Network Recipes in C++, T. Masters, Ed. Morgan Kaufmann, 1993, pp. 47–66. https://doi.org/10.1016/B978-0-08-051433-8.50009-410.1016/B978-0-08-051433-8.50009-4
Search in Google Scholar Back to article
[12] M. Sangiorgio and F. Dercole, “Robustness of LSTM Neural Networks for Multi-Step Forecasting of Chaotic Time Series,” Chaos, Solitons & Fractals, vol. 139, 110045, 2020. https://doi.org/10.1016/j.chaos.2020.11004510.1016/j.chaos.2020.110045
Search in Google Scholar Back to article
[13] R. Kneusel, Random Numbers and Computers. Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-77697-210.1007/978-3-319-77697-2
Search in Google Scholar Back to article
[14] B. Quenneville and C. Gagné, “Testing Time Series Data Compatibility for Benchmarking,” International Journal of Forecasting, vol. 29, iss. 4, pp. 754–766, 2013. https://doi.org/10.1016/j.ijforecast.2011.10.00110.1016/j.ijforecast.2011.10.001
Search in Google Scholar Back to article
[15] A. Stepchenko, J. Chizhov, L. Aleksejeva, and J. Tolujew, “Nonlinear, Non-Stationary and Seasonal Time Series Forecasting Using Different Methods Coupled with Data Preprocessing,” Procedia Computer Science, vol. 104, pp. 578–585, 2017. https://doi.org/10.1016/j.procs.2017.01.17510.1016/j.procs.2017.01.175
Search in Google Scholar Back to article
[16] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting. Springer, Cham, 2016. https://doi.org/10.1007/978-3-319-29854-210.1007/978-3-319-29854-2
Search in Google Scholar Back to article
[17] R. E. Edwards, Functional Analysis. Theory and Applications. Hold, Rinehart and Winston, 1965.
Search in Google Scholar Back to article
[18] A. Jeffrey, “Chapter 27 – Numerical Approximation,” in Handbook of Mathematical Formulas and Integrals, 3rd ed., A. Jeffrey, Ed. Academic Press, 2004, pp. 409–417. https://doi.org/10.1016/B978-012382256-7/50030-010.1016/B978-012382256-7/50030-0
Search in Google Scholar Back to article
[19] W. R. Madych, “Error Estimates for Interpolation by Generalized Splines,” in Curves and Surfaces, P.-J. Laurent, A. Le Méhauté, L. L. Schumaker, Eds. Academic Press, 1991, pp. 297–306. https://doi.org/10.1016/B978-0-12-438660-0.50047-910.1016/B978-0-12-438660-0.50047-9
Search in Google Scholar Back to article