COMPUTER DOMAIN
Published October 27, 2023 | Version v1
Journal article Open

A Study in Time Series Forecasting Model

Authors/Creators

Description

There are many different time series forecasting models available today, and each one needs the correct data pretreatment and analysis to produce a useful prediction. The purpose of this report is to conduct a comparative analysis of the most popular Time Series estimators in order to highlight  the growing interestin time series forecasting techniques. Time series data are being produced in a variety of fields more and more. It supports the growth of time series research and provides data for the study of time series analysis methods. This study makes an effort to classify and cover the current modelling approaches for time seriesdata. Additionally, we contrast variousapproaches and providea list of futurepossibilities for time series forecasting.

Files

A Study n Time Series Forecasting Model -Formatted Paper.pdf

Files (717.5 kB)

Additional details

References

  • 1. ZHENYU LIU, ZHENGTONG ZHU, JING GAO, AND
  • 2. CHENG XU, "Forecast Methods for Time Series Data: A Survey", vol.9 2021.
  • 3. Yacine Ben Baccar," Comparative Study on Time Series Analysis", Thesis.
  • 4. T. Bollerslev, ''Generalized autoregressive conditional heteroskedasticity,'' J. Econometrics, vol. 31, no. 3, pp. 307– 327, Apr. 1986.
  • 5. G. U. Yule, ''On a method of investigating periodicities in distributed series, with special reference to Wolfer's sunspot numbers,'' Philos. Trans. Roy. Soc. London A, vol. 226, nos. 636–646, Jan. 1927, doi: 10.1098/rsta.1927.0007.
  • 6. H. Wold, ''On prediction in stationary time series,'' Ann. Math. Statist., vol. 19, no. 4, pp. 558–567, 1948.
  • 7. G. Janacek, ''Time series analysis forecasting and control,'' J. Time Ser. Anal., vol. 3, no. 3, pp. 131–133, Dec. 2009. [7] M. M. Rounaghi and F. N. Zadeh, ''Investigation of market efficiency and financial stability between S&P 500 and London stock exchange: Monthly and yearly forecasting of time series stock returns using ARMA model,'' Phys. A, Stat. Mech. Appl., vol. 456, pp. 10–21, Aug. 2016.
  • 8. J.-F. Chen, W.-M. Wang, and C.-M. Huang, ''Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting,'' Electr. Power Syst. Res., vol. 34, no. 3, pp. 187–196, Sep. 1995.
  • 9. Y. Zheng, ''Predictive study of tuberculosis incidence by ARMA model combined with air pollution variables,'' Complexity, vol. 2020, May 2020, Art. no. 3619063.
  • 10. X. Y. Lu and L. H. Wang, ''Bootstrap prediction interval for ARMA models with unknown orders,'' REVSTAT-Stat. J., vol. 18, no. 3, pp. 96–375, 2020.
  • 11. A. Inoue, ''Closed-form expression for finite predictor coefficients of multivariate ARMA processes,'' J. Multivariate Anal., vol. 1761, pp. 44–52, Mar. 2020.