Số phát hành: Số 23 - Tháng 8 - 2026Quản trị - Quản lý
A comparative prediction interval analysis on classical forecasting with bootstrapping to enhance accuracy
Xuất bản: tháng 9 22, 2026
Tóm tắt
Time-series forecasting is widely used in economics and plastics manufacturing, where prediction intervals are essential for quantifying forecast uncertainty and supporting informed decision-making. This study develops a distribution-free bootstrap procedure for constructing prediction intervals for classical time-series forecasting models. Unlike conventional approaches, which typically assume Gaussian errors and emphasize point forecasts, the proposed method generates empirical estimates of predictive uncertainty while enhancing forecast performance. Applied to a case study in plastics manufacturing, the procedure improves overall forecasting accuracy by 9.29%.
Bandara, K., Hyndman, R. J., & Bergmeir, C. (2021). MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. arXiv. https://doi.org/10.48550/arXiv.2107.13462
Baumann, E., Hsu, C., Buba, H., & Cox, T. (2023). An introductory approach to time-series data preparation and analysis. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3561
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (2nd ed.). Holden-Day.
4.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on Loess. Journal of Official Statistics, 6(1), 3–73.
5.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Chapman & Hall.
6.
Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359–378.
7.
Harvey, A. C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge University Press.
8.
Hipel, K. W., & McLeod, A. I. (1994). Seasonal autoregressive integrated moving average models. In Developments in water science (Vol. 45, pp. 419–462). Elsevier.
9.
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
10.
Kumar, P., Verma, S., Varma, M., Yadav, K. K., & Singh, A. K. (2024). Comparative analysis of time series forecasting: Traditional statistical approaches vs. machine learning methods. International Journal of Agriculture Extension and Social Development, 7(9), 192–198. https://doi.org/10.33545/26180723.2024.v7.i9c.1054
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting (2nd ed.). Wiley.
12.
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
