Predikce v časových řadách pomocí strojového učení
| Název práce v češtině: | Predikce v časových řadách pomocí strojového učení |
|---|---|
| Název v anglickém jazyce: | Predictions in Time Series With Machine Learning |
| Klíčová slova: | predikce|časová řada|strojové učení|bootstrap|předpověď|hlboké učení|resampling metody|predikční intervaly |
| Klíčová slova anglicky: | prediction|time series|machine learning|bootstrap|forecast|deep learning|resampling methods|prediction intervals |
| Akademický rok vypsání: | 2024/2025 |
| Typ práce: | disertační práce |
| Jazyk práce: | |
| Ústav: | Katedra pravděpodobnosti a matematické statistiky (32-KPMS) |
| Vedoucí / školitel: | doc. RNDr. Michal Pešta, Ph.D. |
| Řešitel: |
| Zásady pro vypracování |
| The research will be devoted to stochastic prediction in time series utilizing various artificial intelligence and data science methods. Statistical learning and justified machine learning techniques like bootstrap or deep learning will be employed.
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| Seznam odborné literatury |
| [1] Alonso, A. M., Peña, D., and Romo, J. (2002). Forecasting Time Series With Sieve Bootstrap, Journal of Statistical Planning and Inference, 100, 1–11
[2] Antoniadis, A., Brossat, X., Cugliari, J., and Poggi, J.-M. (2016). A Prediction Interval for a Function-Valued Forecast Model: Application to Load Forecasting, International Journal of Forecasting, 32, 939–947 [3] Antoniadis, A., Paparoditis, E., and Sapatinas, T. (2006). A Functional Wavelet-Kernel Approach for Time Series Prediction, Journal of the Royal Statistical Society, Series B, 68, 837–857 [4] Aue, A., Norinho, D. D., and Hörmann, S. (2015). On the Prediction of Stationary Functional Time Series, Journal of the American Statistical Association: Theory and Methods, 110, 378–392 [5] Bosq, D. (2000). Linear Processes in Function Spaces, Lecture notes in Statistics, New York: Springer [6] Bosq, D., and Blanke, D. (2007). Inference and Prediction in Large Dimensions, Chichester: Wiley [7] Breidt, F. J., Davis, R. A., and Dunsmuir, W. T. M. (1995). Improved Bootstrap Prediction Intervals for Autoregressions, Journal of Time Series Analysis, 16, 177–200 [8] Dehling, H., Sharipov, S. O., and Wendler, M. (2015). Bootstrap for Dependent Hilbert Space-Valued Random Variables With Application to Von Mises Statistics, Journal of Multivariate Analysis, 133, 200–215 [9] Franke, J., Kreiss, J.-P., Mammen, E., and Neumann, M. H., (2002). Properties of the Nonparametric Autoregressive Bootstrap. Journal of Time Series Analysis, 23(5), 555–585 [10] Hörmann, S., and Kokoszka, P. (2010). Weakly Dependent Functional Data, Annals of Statistics, 38, 1845–1884 [11] Meyer, M., and Kreiss, J.-P. (2015). On the Vector Autoregressive Sieve Bootstrap, Journal of Time Series Analysis, 36, 377–397 [12] Pan, L., and Politis, D. N. (2016). Bootstrap Prediction Intervals for Linear, Nonlinear and Nonparametric Autoregression, Journal of Statistical Planning and Inference, 177, 1–27 [13] Paparoditis, E. (2018). Sieve Bootstrap for Functional Time Series, Annals of Statistics, 46, 3510–3538 [14] Pascual, L., Romo, J., and Ruiz, E. (2004). Bootstrap Predictive Inference of ARIMA Processes, Journal of Time Series Analysis, 25, 449–465 [15] Shang, H. L. (2018). Bootstrap Methods for Stationary Functional Time Series, Statistics and Computing, 28, 1–10 [16] Thombs, L. A., and Schucany, W. R. (1990). Bootstrap Prediction Intervals for Autoregression, Journal of the American Statistical Association: Theory and Methods, 85, 486–492 |