[
Alonso, A., M., Pena, D., and Romo, J. (2006). Introducing model uncertainty by moving block bootstrap. Statistical Papers, 47, 167-179.10.1007/s00362-005-0282-7
]Search in Google Scholar
[
Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625.10.1111/1468-0262.00418
]Search in Google Scholar
[
Armstrong, D. S. (2013). Nonlinear time series and the stationary bootstrap. San Diego State University.
]Search in Google Scholar
[
Awajan, M., A., Ismail, M., T., and Alwadi, S. (2017). Forecasting Time Series Data Using EMD-HW Bagging. International Journal of Statistics and Economics, 18, 9-21.
]Search in Google Scholar
[
Bergmeir, C., Hyndman, R., J., and Benitez, M. J. (2016). Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation. International Journal of Forecasting, 32, 303-312.10.1016/j.ijforecast.2015.07.002
]Search in Google Scholar
[
Cao, W., Sun, S., and Li, H. (2021). A new forecasting system for high-speed railway passenger demand based on residual component disposing. Measurement, 183.10.1016/j.measurement.2021.109762
]Search in Google Scholar
[
Carlstein, E., Do, K.-A., Hall, P., Hesterberg, T., and Künsch, H. (1998). Matched-Block bootstrap for dependent data. Bernoulli, 4, 305-328.10.2307/3318719
]Search in Google Scholar
[
Cordeiro, C., and Neves, M. M. (2006). The bootstrap methodology in time series forecasting. Retrieved from https://www.researchgate.net/publication/259487568_The_bootstrap_methodology_in_time_series_forecasting
]Search in Google Scholar
[
Dhiyanji, M., and Sundaravadivu, K. (2016). Application of soft computing technique in the modelling and prediction of gold and silver rates. Journal of Advances in Technology and Engineering Research, 2(4), 118-124.
]Search in Google Scholar
[
Doodley, G., and Lenihan, H. (2005). An assessment of time series methods in metal price forecasting. Resources Policy, 30(3), 2008-2017.
]Search in Google Scholar
[
Dudek, E. A. (2013). Circular block bootstrap for coefficients of autocovariance function of almost periodically correlated time series. Metrika, 78, 313-335.10.1007/s00184-014-0505-9
]Search in Google Scholar
[
Dudek, E. A. (2016). First and second order analysis for periodic random arrays using block bootstrap methods. Electronic Journal of Statistics, 10, 2561-2583.10.1214/16-EJS1182
]Search in Google Scholar
[
Dudek, G. (2012). Modele ARIMA do krótkoterminowego prognozowania obciążeń systemów elektroenergetycznych. Rynek Energii, 2, 1-6.
]Search in Google Scholar
[
Elmore, L. K., Baldwin, M. E., and Schultz M. D. (2005). Field significance revisited: Spatial bias errors in forecasts as applied to the eta model. Monthly Weather Review, 134(2), 519-531.10.1175/MWR3077.1
]Search in Google Scholar
[
Ganczarek-Gamrot, A. (2014). Analiza szeregów czasowych. Katowice: Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach.
]Search in Google Scholar
[
He, K., Chen, Y., and Tso, G. K. F. (2017). Price forecasting in the precious metal market: A multivariate EMD denoising approach. Resources Policy, 54, 9-24.10.1016/j.resourpol.2017.08.006
]Search in Google Scholar
[
He, K., Lu, X., Zou, Y., and Lai, K. K., (2015). Forecasting metal prices with a curvelet based multiscale methodology. Resources Policy, 45, 144-150.10.1016/j.resourpol.2015.03.011
]Search in Google Scholar
[
Henriette de Koster, F. (1999). The bootstrap approach to autoregressive time series analysis. Retrieved from https://repository.up.ac.za/bitstream/handle/2263/29608/dissertation.pdf;sequence=1
]Search in Google Scholar
[
Hounyou, U. (2014). The wild tapered block bootstrap. Retrieved from https://econ.au.dk/fileadmin/site_files/filer_oekonomi/Working_Papers/CREATES/2014/rp14_32.pdf
]Search in Google Scholar
[
Jorsten, R. (2007). Bootstrap. Retrieved from http://www.stat.rutgers.edu/home/rebecka/Stat565/bootstrap.pdf
]Search in Google Scholar
[
Kasprzyk-Czelej, K. (2018). Długookresowa zależność cen metali szlachetnych i ropy naftowej. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 370, 27-50.
]Search in Google Scholar
[
Kończak, G., and Miłek, M. (2014). Wykorzystanie metody moving block bootstrap w prognozowaniu szeregów czasowych z wahaniami okresowymi. Studia Ekonomiczne, 203, 91-100.
]Search in Google Scholar
[
Kowalczyk, M. (2021, November, 26). Inwestycja w srebro w 2022 r. – wszystko, co musisz wiedzieć. Retrieved from https://www.najlepszekonto.pl/inwestycja-w-srebro
]Search in Google Scholar
[
Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17(3), 1217-1241.10.1214/aos/1176347265
]Search in Google Scholar
[
Lahiri, S. N. (2003). Selecting optimal block lengths for block bootstrap methods. Department of Statistics Iowa State University. Retrived from https://www.interfacesymposia.org/I03/I2003Proceedings/LahiriSoumendra/LahiriSoumendra.paper.pdf
]Search in Google Scholar
[
Li, W., Cheng Y., and Fang, Q. (2020). Forecast on silver futures linked with structural breaks and day-of-the-week effect. North American Journal of Economics and Finance, 53.10.1016/j.najef.2020.101192
]Search in Google Scholar
[
Li, L., Wang, Y., and Li, X. (2020). Tourists forecast Lanzhou based on the Baolan high-speed railway by the ARIMA model. Applied Mathematics and Nonlinear Sciences, 5, 55-60.10.2478/amns.2020.1.00006
]Search in Google Scholar
[
Milenković, M., Vadlenka, L., Melichar, V., Bojović, N., and Avramović, Z. (2018). SARIMA modelling approach for railway passenger flow forecasting, Transport, 33, 1113-1120.
]Search in Google Scholar
[
Niu, M., Wang, Y., Sun, S., and Li, Y. (2016). A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting. Atmospheric Environment, 134, 168-180.10.1016/j.atmosenv.2016.03.056
]Search in Google Scholar
[
Nordman, J. D., and Lahiri, S. N. (2012). Block bootstraps for time series with fixed regressors. Journal of the American Statistical Association, 107(497), 233-246.10.1080/01621459.2011.646929
]Search in Google Scholar
[
Nordman, J. D., and Lahiri, N. S. (2007). Optimal block size for variance estimation by a spatial block bootstrap method. The Indian Journal of Statistics, 69, 468-493.
]Search in Google Scholar
[
Paparoditis, E., and Politis, D. N. (2001). Tapered block bootstrap. Biometrika, 88, 1105-1119.10.1093/biomet/88.4.1105
]Search in Google Scholar
[
Parisi, A., Parisi, F., and Diaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational Financial Management, 18, 477-487.10.1016/j.mulfin.2007.12.002
]Search in Google Scholar
[
Patton, A., Politis, N. D., and Halbert, W. (2009). Correction to “Automatic block-length selection for the dependent bootstrap” by D. Politis and H. White. Econometric Reviews, 28, 372-375.10.1080/07474930802459016
]Search in Google Scholar
[
Pierdzioch, C., and Risse, M. (2017). Forecasting precious metal returns with multivariate random forests. Empirical Economics, 58, 1167-1184.10.1007/s00181-018-1558-9
]Search in Google Scholar
[
Politis, N., D., and Romano, P. (1991). A circular block resampling procedure for stationary data. Department of Statistics Purdue University.
]Search in Google Scholar
[
Politis, N. D., and Romano, P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89, (428), 1303-1313.10.1080/01621459.1994.10476870
]Search in Google Scholar
[
Politis, N. D., and White, H. (2006). Automatic block-length selection for the dependent bootstrap. Econometric Reviews, 23, 53-70.10.1081/ETC-120028836
]Search in Google Scholar
[
Qu, L., Li, W., Li, W., Ma, D., and Wang, Y. (2019). Daily long-term traffic flow forecasting based on a deep neural network. Expert Systems with Applications, 121, 304-312.10.1016/j.eswa.2018.12.031
]Search in Google Scholar
[
Radovanov, B., and Marcikić, A. (2017). Bootstrap testing of trading strategies in emerging Balkan stock markets. E&M Economics and Management, 20(4), 103-119.10.15240/tul/001/2017-4-008
]Search in Google Scholar
[
Smith, B. L., Demetsky, M. J. (1994). Short-term traffic flow prediction: Neural network approach. Transportation Research Record, 1453, 98-104.
]Search in Google Scholar
[
Vogel, R. M., and Shallcross, A. M. (1996). The moving blocks bootstrap versus parametric time series models. Water Resources Research, 32(6), 1875-1992.10.1029/96WR00928
]Search in Google Scholar
[
Włodarczyk, B., and Miciuła, I. (2020). Empirical analysis of long memory and asymmetry effects for the effectiveness of forecasting volatility of returns on the commodity market based on the example of gold and silver. E&M Economics and Management, 23(2), 126-143.10.15240/tul/001/2020-2-009
]Search in Google Scholar
[
Xie, M. Q., Li, X. M., Zhou, W. L., and Fu, Y. B. (2014). Forecasting the short-term passenger flow on high-speed railway with neural networks. Retrieved from https://www.hindawi.com/journals/cin/2014/375487/10.1155/2014/375487
]Search in Google Scholar
[
Xu, Z., Huang, J., and Jiang, F. (2017). Subsidy competition, industrial land price distortions and overinvestment: Empirical evidence from China’s manufacturing enterprises. Applied Economics, 49(48), 4851-4870.10.1080/00036846.2017.1296547
]Search in Google Scholar