Automated Detection of Structural Change in Botswana Gross Domestic Product (GDP) using Novel Algorithm.
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Abstract
The main objective of this study is to examine the phenomenon, Structural change in the gross domestic product of Botswana and possible forecast and recommend for future adjustment. Gross fixed capital formation (% of GDP). Singapore Gross Domestic Product (GDP) data and some resource materials were obtained from the data stream of Universiti Utara Malaysia and National University Singapore library. In the methodology, BFAST (Break for additive, Season and trend) and BFTSC (Break for time series components) was used to examine the structural change, time series components present in the data (Botswana GDP) using R and Python software. BFTSC was created to capture the trend, seasonal, cyclical and irregular components as a combined image and to present them in a single plot. The result obtained, model acquired from the components (pattern) extracted using BFTSC was subsequently process for forecasting purposes and recommendation follows. The real data findings suggested that BFTSC can provide a better time series components identification better than manual process and hence should be taken serious. Botswana GDP is growing declining, caution should be taken to improve the GDP such that Botswana can have bounty reserve and can also loan poor and financial weak countries. Improvement Botswana GDP is recommended.
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References
Ajare, E.O., & Ismail, S. (2019). Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC) in Identification of Time Series Components in Univariate Forecasting. Advanced Research in Dynamical and Control Systems, Volume 11, 05-Special Issue 2019, Pages: 995-1004. http://www.jardcs.org/special-issue.php.
Ajare, E.O., & Ismail, S. (2019). Simulation of Data to Contain the Four Time Series Components in Univariate Forecasting. Advanced Research in Dynamical and Control Systems, Volume 11, 05-Special Issue, 2019 Pages: 1005-1010. http://www.jardcs.org/special-issue.php. Google scholar.
Ajare E. O. and Ismail .S. (2019). Comparative study of Manual time series components identification with automated Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC) in Identification in Univariate Forecasting. Published by TEST, Engineering and Management Journal.November-December 2019 ISSN: 0193-4120 Page No. 2826 – 2844. Publication Issue: November-
December 2019
Ajare. E.O and Adefabi .A (2019). Group for Time Series Components (GFTSC) Identification of Gross Domestic Product (GDP) of United Kingdom (UK ). International Journal of Innovative Science and Research Technology. Academia.edu, Google search.Volume 8, Issue 7, July 2023, IJISRT1410, ISSN NO;2456-2165. DOI : https://doi.org/10.5281/zenodo.8304849.
Ajare. E, Adefabi and Adeyemo. A (2020). Examining the Efficacy of Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC) with Volatile Simulated and Empirical Data. Asian Journal of Probability and Statistics. Academia.edu, Google search. Volume 8, Issue 7, July 2023, AJPAS 103577, 2023, ISSN NO; 2456-2165
Abbes, A. B., & Farah, I. R. (2017). Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM. In Handbook of Research on Geographic Information Systems Applications and Advancements (pp. 387-406). IGI Global.
Adewoye, R., & Chapman, h. (2018) testing spectral variation hypothesis on the afromontane forest ecosystem of ngelnyaki, north eastern nigeria with landsat 8 (oli) and macro-ecological data
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: forecasting and control, revised ed. Holden-Day. Oakland, California.
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of applied econometrics, 18(1), 1-22.
Bornhorst, F., Dobrescu, G., Fedelino, A., Gottschalk, J., & Nakata, T. (2011). When and how to adjust beyond the business cycle? A guide to structural fiscal balances. IMF Technical Notes and Manuals, 11(02).
Bohn, H. (1995). The sustainability of budget deficits in a stochastic economy. Journal of Money, Credit and Banking, 27(1), 257-271.
Buhalau, T. (2016). Detecting clear-cut deforestation using Landsat data: A time series analysis of remote sensing data in Covasna County, Romania between 2005 and 2015. Student thesis series INES.
Cesta, A., Cortellessa, G., Pecora, F., & Rasconi, R. (2005, May). Monitoring Domestic Activities with Scheduling Techniques. In Proceedings of the 2nd.
Cleveland, W. P., & Tiao, G. C. (1976). Decomposition of seasonal time series: A model for the Census X-11 program. Journal of the American statistical Association, 71(355), 581-587.
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57(3), 359-377.
Caiado, J. (2009). Performance of combined double seasonal univariate time series models for forecasting water demand. Journal of Hydrologic Engineering, 15(3), 215-222. Cipra, T., & Romera, R. (1997). Kalman filter with outliers and missing observations. Test, 6(2), 379-395.
Cipra, T., & Romera, R. (1997). Kalman filter with outliers and missing observations. Test, 6(2), 379-395.
DeVries, B., Pratihast, A. K., Verbesselt, J., Kooistra, L., de Bruin, S., & Herold, M. (2013, June). Near real- time tropical forest disturbance monitoring using Landsat time series and local expert monitoring data. In Analysis of Multi-temporal Remote Sensing Images, MultiTemp 2013: 7th International Workshop on the (pp. 1-4). IEEE
Ewing, B. T., & Malik, F. (2013). Volatility transmission between gold and oil futures under structural breaks.
International Review of Economics & Finance, 25, 113-121.journal homepage: www.elsevier.com/locate/iref
Flicek, P., & Birney, E. (2009). Sense from sequence reads: methods for alignment and assembly. Nature methods, 6(11s), S6.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine:
Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27
Jong, R., Verbesselt, J., Schaepman, M. E., & Bruin, S. (2012). Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change. Global Change Biology, 18(2), 642-655. DOI: 10.1111/j.1365-2486.2011.02578.x
Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4-37. DOI: 10.1109/34.824819
Jamali, S., Jönsson, P., Eklundh, L., Ardö, J., & Seaquist, J. (2015). Detecting changes in vegetation trends using time series segmentation. Remote Sensing of Environment, 156, 182-195.
doi.org/10.1016/j.rse.2014.09.010
Porter, J., & Zhang, L. (2018). BisPin and BFAST-Gap: Mapping bisulfite-treated reads. bioRxiv, 284596.
Tolsheden, J. (2018). Detecting and testing for Seasonal breaks in Quarterly National accounts: Based on X- 12-ARIMA and BFAST Methods.
Maggi, L. M. B. (2018). Times Series Analysis. In Multiscale Forecasting Models (pp. 1-29). Springer,
Mok, T. S., Wu, Y. L., Ahn, M. J., Garassino, M. C., Kim, H. R., Ramalingam, S. S., ... & Lee, C. K. (2017). Osimertinib or platinum–pemetrexed in EGFR T790M–positive lung cancer. New England Journal of Medicine, 376(7), 629-640.
Maus, V., Câmara, G., Appel, M., & Pebesma, E. (2017). dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R. Journal of Statistical Software.
Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment, 114(1), 106-115. doi.org/10.1016/j.rse.2009.08.014
Rikus, L. (2018). A simple climatology of westerly jet streams in global reanalysis datasets part 1: mid-latitude upper tropospheric jets. Climate Dynamics, 50(7-8), 2285-2310.
Verbesselt, J., Zeileis, A., Hyndman, R., & Verbesselt, M. J. (2012). Package ‘bfast’
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle- consistent adversarial networks. arXiv preprint.
Zewdie, W., Csaplovics, E., & Inostroza, L. (2017). Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dry land vegetation variability. Applied Geography, 79, 167-178.
Zdravevski, E., Lameski, P., Mingov, R., Kulakov, A., & Gjorgjevikj, D. (2015, September). Robust histogram-based feature engineering of time series data. In Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on (pp. 381-388). IEEE.
Zhao, G., Li, E., Mu, X., Wen, Z., Rayburg, S., & Tian, P. (2015). Changing trends and regime shift of streamflow in the Yellow River basin. Stochastic environmental research and risk assessment, 29(5), 1331- 1343.
Zeileis, A., Kleiber, C., Krämer, W., & Hornik, K. (2003). Testing and dating of structural changes in practice. Computational Statistics & Data Analysis, 44(1-2), 109-123.
CIA World Fact Book history,: Brief book on basic intelligence and world fact book 2019.
www.cia.gov>about>hisory>