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Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Yıl 2022, Cilt 13, Sayı 2, 199 - 204, 28.06.2022
https://doi.org/10.24012/dumf.1079230

Öz

Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series short-term prediction processes. In this study, the performances of both stand-alone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equi-weighted (EW), variable-weighted (VW) and cross-validation-weighted (CVW) for hybrid operation were also compared. The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the non-combined model.

Kaynakça

  • [1] E. Gjika, A. Ferrja, and A. Kamberi, “A Study on the Efficiency of Hybrid Models in Forecasting Precipitations and Water Inflow Albania Case Study,” Adv. Sci. Technol. Eng. Syst. J., vol. 4, no. 1, pp. 302–310, 2019.
  • [2] P. N. Tattar, Hands-on Ensemble Learning with R. Birminghami, Mumbai: Packt Publishing Ltd, 2018.
  • [3] S. Smyl, “A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting,” Int. J. Forecast., vol. 36, no. 1, pp. 75–85, 2020.
  • [4] F. Yu and X. Xu, “A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network,” Appl. Energy, vol. 134, pp. 102–113, 2014.
  • [5] Z. Pala, “Examining EMF Time Series Using Prediction Algorithms With R,” vol. 44, no. 2, pp. 223–227, 2021.
  • [6] Z. Pala and M. Şana, “Attackdet: Combining web data parsing and real-time analysis with machine learning,” J. Adv. Technol. Eng. Res., vol. 6, no. 1, pp. 37–45, 2020.
  • [7] Z. Pala and R. Atici, “Forecasting Sunspot Time Series Using Deep Learning Methods,” Sol. Phys., vol. 294, no. 5, 2019.
  • [8] Z. Pala and A. F. Pala, “Comparison of ongoing COVID-19 pandemic confirmed cases / deaths weekly forecasts on continental basis using R statistical models,” Dicle Univ. J. Eng., vol. 4, pp. 635–644, 2021.
  • [9] Z. Pala and O. Özkan, “Artificial Intelligence Helps Protect Smart Homes against Thieves,” DÜMF Mühendislik Derg., vol. 11, no. 3, pp. 945–952, 2020.
  • [10] V. Makridakis, S., Spiliotis, E., Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward,” PLoS One, vol. 13, no. 3, p. e0194889, 2018.
  • [11] Z. Pala, İ. H. Ünlük, and E. Yaldız, “Forecasting of electromagnetic radiation time series: An empirical comparative approach,” Appl. Comput. Electromagn. Soc. J., vol. 34, no. 8, pp. 1238–1241, 2019.
  • [12] M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett., vol. 42, no. 1, pp. 11–24, 2014.
  • [13] D. Gidon, X. Pei, A. D. Bonzanini, D. B. Graves, and A. Mesbah, “Machine Learning for Real-time Diagnostics of Cold Atmospheric Plasma Sources,” no. April, 2019.
  • [14] J. Liu, S. Wang, N. Wei, X. Chen, H. Xie, and J. Wang, “Natural gas consumption forecasting: A discussion on forecasting history and future challenges,” J. Nat. Gas Sci. Eng., vol. 90, no. January, p. 103930, 2021.
  • [15] W. Qiao, K. Huang, M. Azimi, and S. Han, “A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine,” IEEE Access, vol. 7, pp. 88218–88230, 2019.
  • [16] P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
  • [17] T. Ma, C. Antoniou, and T. Toledo, “Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast,” Transp. Res. Part C Emerg. Technol., vol. 111, no. January, pp. 352–372, 2020.
  • [18] O. Castillo and P. Melin, “Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic,” Chaos, Solitons and Fractals, vol. 140, p. 110242, Nov. 2020.
  • [19] J. Li, Q. Wu, Y. Tian, and L. Fan, “Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network,” Energy, vol. 227, p. 120478, 2021.
  • [20] F. Gao and X. Shao, “Forecasting annual natural gas consumption via the application of a novel hybrid model,” Environ. Sci. Pollut. Res., vol. 28, no. 17, pp. 21411–21424, 2021.
  • [21] R. Atıcı and Z. Pala, “Prediction of the Ionospheric foF2 Parameter Using R Language Forecasthybrid Model Library Convenient Time,” Wirel. Pers. Commun., no. doi.org/10.1007/s11277-021-09050-6 Prediction, pp. 1–20, 2021.
  • [22] F. M. Tseng, H. C. Yu, and G. H. Tzeng, “Applied hybrid grey model to forecast seasonal time series,” Technol. Forecast. Soc. Change, vol. 67, no. 2–3, pp. 291–302, 2001.
  • [23] Z. Chang, Y. Zhang, and W. Chen, “Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform,” Energy, vol. 187, p. 115804, 2019.
  • [24] P. Du, J. Wang, W. Yang, and T. Niu, “A novel hybrid model for short-term wind power forecasting,” Appl. Soft Comput. J., vol. 80, pp. 93–106, 2019.
  • [25] E. Meira, F. L. C. Oliveira, and L. M. de Menezes, “Forecasting natural gas consumption using Bagging and modified regularization techniques,” Energy Econ., vol. 106, no. January, p. 105760, 2022.
  • [26] Z. Pala, “Using forecastHybrid Package to Ensemble Forecast Functions in the R,” Int. Conf. Data Sci. Mach. Learn. Stat. - 2019, vol. 1, no. 1, pp. 45–47, 2019.
  • [27] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, Oct. 2006.
  • [28] S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int. J. Forecast., vol. 32, no. 3, pp. 669–679, 2016.
  • [29] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 time series and 61 forecasting methods,” Int. J. Forecast., vol. 36, no. 1, pp. 54–74, Jan. 2020.

Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment

Yıl 2022, Cilt 13, Sayı 2, 199 - 204, 28.06.2022
https://doi.org/10.24012/dumf.1079230

Öz

Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series short-term prediction processes. In this study, the performances of both stand-alone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equi-weighted (EW), variable-weighted (VW) and cross-validation-weighted (CVW) for hybrid operation were also compared. The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the non-combined model.

Kaynakça

  • [1] E. Gjika, A. Ferrja, and A. Kamberi, “A Study on the Efficiency of Hybrid Models in Forecasting Precipitations and Water Inflow Albania Case Study,” Adv. Sci. Technol. Eng. Syst. J., vol. 4, no. 1, pp. 302–310, 2019.
  • [2] P. N. Tattar, Hands-on Ensemble Learning with R. Birminghami, Mumbai: Packt Publishing Ltd, 2018.
  • [3] S. Smyl, “A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting,” Int. J. Forecast., vol. 36, no. 1, pp. 75–85, 2020.
  • [4] F. Yu and X. Xu, “A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network,” Appl. Energy, vol. 134, pp. 102–113, 2014.
  • [5] Z. Pala, “Examining EMF Time Series Using Prediction Algorithms With R,” vol. 44, no. 2, pp. 223–227, 2021.
  • [6] Z. Pala and M. Şana, “Attackdet: Combining web data parsing and real-time analysis with machine learning,” J. Adv. Technol. Eng. Res., vol. 6, no. 1, pp. 37–45, 2020.
  • [7] Z. Pala and R. Atici, “Forecasting Sunspot Time Series Using Deep Learning Methods,” Sol. Phys., vol. 294, no. 5, 2019.
  • [8] Z. Pala and A. F. Pala, “Comparison of ongoing COVID-19 pandemic confirmed cases / deaths weekly forecasts on continental basis using R statistical models,” Dicle Univ. J. Eng., vol. 4, pp. 635–644, 2021.
  • [9] Z. Pala and O. Özkan, “Artificial Intelligence Helps Protect Smart Homes against Thieves,” DÜMF Mühendislik Derg., vol. 11, no. 3, pp. 945–952, 2020.
  • [10] V. Makridakis, S., Spiliotis, E., Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward,” PLoS One, vol. 13, no. 3, p. e0194889, 2018.
  • [11] Z. Pala, İ. H. Ünlük, and E. Yaldız, “Forecasting of electromagnetic radiation time series: An empirical comparative approach,” Appl. Comput. Electromagn. Soc. J., vol. 34, no. 8, pp. 1238–1241, 2019.
  • [12] M. Längkvist, L. Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognit. Lett., vol. 42, no. 1, pp. 11–24, 2014.
  • [13] D. Gidon, X. Pei, A. D. Bonzanini, D. B. Graves, and A. Mesbah, “Machine Learning for Real-time Diagnostics of Cold Atmospheric Plasma Sources,” no. April, 2019.
  • [14] J. Liu, S. Wang, N. Wei, X. Chen, H. Xie, and J. Wang, “Natural gas consumption forecasting: A discussion on forecasting history and future challenges,” J. Nat. Gas Sci. Eng., vol. 90, no. January, p. 103930, 2021.
  • [15] W. Qiao, K. Huang, M. Azimi, and S. Han, “A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Whale Optimization Algorithm and Relevance Vector Machine,” IEEE Access, vol. 7, pp. 88218–88230, 2019.
  • [16] P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
  • [17] T. Ma, C. Antoniou, and T. Toledo, “Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast,” Transp. Res. Part C Emerg. Technol., vol. 111, no. January, pp. 352–372, 2020.
  • [18] O. Castillo and P. Melin, “Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic,” Chaos, Solitons and Fractals, vol. 140, p. 110242, Nov. 2020.
  • [19] J. Li, Q. Wu, Y. Tian, and L. Fan, “Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network,” Energy, vol. 227, p. 120478, 2021.
  • [20] F. Gao and X. Shao, “Forecasting annual natural gas consumption via the application of a novel hybrid model,” Environ. Sci. Pollut. Res., vol. 28, no. 17, pp. 21411–21424, 2021.
  • [21] R. Atıcı and Z. Pala, “Prediction of the Ionospheric foF2 Parameter Using R Language Forecasthybrid Model Library Convenient Time,” Wirel. Pers. Commun., no. doi.org/10.1007/s11277-021-09050-6 Prediction, pp. 1–20, 2021.
  • [22] F. M. Tseng, H. C. Yu, and G. H. Tzeng, “Applied hybrid grey model to forecast seasonal time series,” Technol. Forecast. Soc. Change, vol. 67, no. 2–3, pp. 291–302, 2001.
  • [23] Z. Chang, Y. Zhang, and W. Chen, “Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform,” Energy, vol. 187, p. 115804, 2019.
  • [24] P. Du, J. Wang, W. Yang, and T. Niu, “A novel hybrid model for short-term wind power forecasting,” Appl. Soft Comput. J., vol. 80, pp. 93–106, 2019.
  • [25] E. Meira, F. L. C. Oliveira, and L. M. de Menezes, “Forecasting natural gas consumption using Bagging and modified regularization techniques,” Energy Econ., vol. 106, no. January, p. 105760, 2022.
  • [26] Z. Pala, “Using forecastHybrid Package to Ensemble Forecast Functions in the R,” Int. Conf. Data Sci. Mach. Learn. Stat. - 2019, vol. 1, no. 1, pp. 45–47, 2019.
  • [27] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, Oct. 2006.
  • [28] S. Kim and H. Kim, “A new metric of absolute percentage error for intermittent demand forecasts,” Int. J. Forecast., vol. 32, no. 3, pp. 669–679, 2016.
  • [29] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 time series and 61 forecasting methods,” Int. J. Forecast., vol. 36, no. 1, pp. 54–74, Jan. 2020.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik, Ortak Disiplinler
Bölüm Makaleler
Yazarlar

Zeydin PALA> (Sorumlu Yazar)
MUŞ ALPARSLAN ÜNİVERSİTESİ
0000-0002-2642-7788
Türkiye


İbrahim Halil ÜNLÜK>
MUŞ ALPARSLAN ÜNİVERSİTESİ
0000-0002-6541-2714
Türkiye

Destekleyen Kurum -
Proje Numarası -
Teşekkür -
Erken Görünüm Tarihi 28 Haziran 2022
Yayımlanma Tarihi 28 Haziran 2022
Yayınlandığı Sayı Yıl 2022, Cilt 13, Sayı 2

Kaynak Göster

IEEE Z. Pala ve İ. H. Ünlük , "Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment", Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, c. 13, sayı. 2, ss. 199-204, Haz. 2022, doi:10.24012/dumf.1079230
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456