MODELING OF CO2 EMISSION STATISTICS in TURKEY BY FUZZY TIME SERIES ANALYSIS

Abstract

The process of determining the values which a time series will receive in the future is a very important concept. The fuzzy time series method has been widely used in recent years as it is more convenient to process data in small samples which are incomplete and/or ambiguous, and it does not contain any assumptions for time series. In this study, fuzzy time series analysis was used to predict CO2 emission values for Turkey. For this purpose, time series (annual) for total greenhouse gas emissions by sectors (CO2 equivalent) between 1990 and 2016 were analyzed. The main goal of this study is to model greenhouse gas emission statistics in Turkey with fuzzy time series analysis.

Keywords

• Time Series Analysis
• Fuzzy Time Series Analysis
• CO2 emission
• RMSE
• Chen Models
• Gustafson-Kessel clustering algorithm

References

1. [1] Ayvaz B, Kusakçı AO, Temur GT. Energy-related CO2 emission forecast for Turkey and Europe and Eurasia A discrete grey model approach. Grey Systems: Theory and Application 2017; 7(3):,437-4532.
2. [2] Kuşkaya S, Gençoğlu P. A comparison of OECD countries by the years 1995-2015 to green gas Emission analysis: a statistical analysis International Journal of Disciplines Economics and Administrative Sciences Studies, 2017; 3(3):177-188.
3. [3] Tatar V, Özer MB. Effects on climate change of greenhouse gases emissions:current status analysis of Turkey. Journal of Social and Humanities Sciences Research 2018; 5(30):3993-3999.
4. [4] Akın G Global warming, reasons and outcomes. Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi, 2006; 46(2):29-43.
5. [5] Dikmen AÇ. Contributions of electricity generation by the sun and wind on reduction greenhouse gas emissions and environmental costs in Turkey. Turkish Studies, 2019; 14(2):275-293.
6. [6] Abdullah L, Pauzi HM. Methods in forecasting carbon dioxide emissions: A decade review, Jurnal Teknologi, 2015; 75(1):67-82.
7. [7] Özceylan E. Forecasting CO2 Emission of Turkey: Swarm intelligence approaches. International Journal of Global Warming, 2016; 9(3): 337-61.
8. [8] Liu Y, Tian Y, Chen M. Research on the prediction of carbon emission based on the chaos theory and neural network. Internationl Journal of Bioautomation, 2017; 21(4), Special Issue, 339-348.

9. [9] Appiah K, Du J, Appah R, Quacoe D. Prediction of potential carbon dioxide emissions of selected emerging economies using artificial neural network. Journal of Environmental Science and Engineering A, 2018; 7: 321-335.
10. [10] Garip E. Oktay AB. CO2 emisyonunun makine öğrenmesi metotlari ile tahmin edilmesi. Conference: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 262-266, 28-30 September 2018, Malatya, Turkey.
11. [11] Wang L, Zhan L. Li R. Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models Sustainability, 2019; 11, 1-16.
12. [12] Sutthichaimethee P, Apinyar C, Suyaprom S. A forecasting model for economic growth and Co2 emission based on industry 4.0 political policy under the government power: adapting a second-order autoregressive-sem. Journal of Open Innovatin Technolgy Market and Complexity, 2019; 5(3): 69-89.
13. [13] Doroodi M, Mokhtar A. Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran. International Journal of Energy Sector Management, 2019; 13(3):486-499.
14. [14] Oyehan TA, Tawabini BS. Forecasting CO2 emissions in the Persian Gulf States. Global Journal of Environmental. Science and Management, 2017; 3(1): 1-10, Winter 2017. [15] Maleki A, Nasseri S, Aminabad MS, Hadi M. Comparison of ARIMA and NNAR models for forecasting water treatment plant's influent characteristics. KSCE Journal of Civil Engineering, 2018; 22(9):3233-3245.
15. [16] Abd Rahman NH, Lee MH, Talib ML, Suhartono S. Forecasting of air pollution index with artificial neural network. Jurnal Teknologi (Sciences and Engineering), 2013; 63(2):59–64.
16. [17] Karaaslan A, Gezen M. Forecasting of Turkey’s sectoral energy demand by using fuzzy grey regression model. International Journal of Energy Economics and Policy, 2017; 7(1): 67-77.
17. [18] Mahla SK, Parmar KS, , Singh J, Dhir A, Sandhu SS, Chauhan BS., Trend and time series analysis by ARIMA model to predict the emissions and performance characteristics of biogas fueled compression ignition engine, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019; https://doi.org/10.1080/15567036.2019.1670286
18. [19] Atsalakis G, Frantzis D, Zopounidis C Energy’s exports forecasting by a neuro-fuzzy controller. Energy Systems, 2015, 6(2):249-267. DOI 10.1007/s12667-015-0140-1
19. [20] Tavan M. Estimation and forecast of carbon dioxide emission in Iran: Introducing A New Hybrid Modelling. Journal of Economics and Management, 2019; 36 (2) 144-167.
20. [21] Song Q, Chissom BS. Forecasting enrollments with fuzzy time series, Part I. Fuzzy Sets and Systems, 1993-a; 54(1):1-9.
21. [22] Song Q, Chissom BS. Fuzzy time series and its models”, Fuzzy Sets and Systems, 1993-b; 54 (3): 269-277.
22. [23] Chen SM. Forecasting enrollments based on fuzzy time-series. Fuzzy Sets and Systems, 1996, 81(3):311-319.
23. [24] Chen SM. Using high-order fuzzy time series for handling forecasting problems. Proceedings of the 11th National Conference on Information Management, Kaohsiung, Taiwan, 2000; Republic of China, 2000.
24. [25] Chen SM, Hwang JR Temperature predication using fuzzy time series. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 2000; 30(2):263-275. DOI: 10.1109/3477.836375
25. [26] Hwang JR, Chen SM, Lee CH (1998) Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems, l00 (2):2l7- 228. https://doi.org/10.1016/S0165-0114(97)00121-8
26. [27] Song Q, Chissom BS. Forecasting enrollments with fuzzy time series, Part II.. Fuzzy Sets and Systems, 1994; 62(l):1- 8.
27. [28] Song Q, Leland RP. Adaptive learning defuzzification techniques and applications. Fuzzy Sets and Systems, 1996; 81(3):321-329.
28. [29] Song Q, Leland RP, Chissom BS. A new fuzzy time-series model of fuzzy number observations. Fuzzy Sets and Systems, 1995; 73(3):341- 348.
29. [30] Sullivan J, Woodall WH . A Comparison of fuzzy forecasting and markov modeling. Fuzzy Sets and Systems, 1994; 64(3):279- 293.
30. [31] Eğrioğlu E, Aladağ CH, Yolcu U, Uslu VR, Erilli NA. Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering, Expert Systems with Applications, 2011; 38(8), 10355-10357.
31. [32] Chen SM Forecasting enrollments based on highorder fuzzy time series. Cybernetics and Systems: An International Journal, 2002; 33(1): 1-16.
32. [33] Gustafson D, Kessel W. Fuzzy clustering with a fuzzy covariance matrix. Proc. 18th IEEE CDC Conference, San Diego, USA, 1979; 761-766.
33. [34] Balasko B, Abonyi J, Feil B. Fuzzy clustering and data analysis toolbox. 2005; Department of Process Engineering University of Veszprem.