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Yıl 2022, Cilt: 35 Sayı: 3, 1152 - 1169, 01.09.2022
https://doi.org/10.35378/gujs.798423

Öz

Kaynakça

  • [1] Box, G.E.P., Jenkins, G.M., Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, USA, (1970).
  • [2] Topuz, B.K., Bozoglu, M., Baser, U., Eroglu, N. A., “Forecasting of apricot production of Turkey by using Box-Jenkins method”, Turkish Journal of Forecasting. 2(2): 20-26, (2018).
  • [3] Mithiya, D., Datta, L., Mandal, K., “Time series analysis and forecasting of oilseeds production in India: using autoregressive integrated moving average and group method of data handling – neural network”, Asian Journal of Agricultural Extension, Economics & Sociology, 30(2): 1-14, (2019).
  • [4] Galeshchuk, S., “Neural Networks performance in exchange rate prediction”, Neurocomputing. 172: 446-452, (2016).
  • [5] Bas, E., Egrioglu, E., Aladag, C.H., Yolcu, U., “Fuzzy time series network used to forecast linear and nonlinear time series”, Applied Intelligence, 43: 343-355, (2015).
  • [6] Akdeniz, E., Egrioglu, E., Bas, E., Yolcu, U., “An ARMA type pi-sigma artificial neural network for nonlinear time series forecasting”, Journal of Artificial Intelligence and Soft Computing Research, 8(2): 121-132, (2018).
  • [7] Jiang, P., Dong, Q., Li, P.,“A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction”, Applied Soft Computing, 55: 44-62, (2017).
  • [8] Yong, N.K., Awang, N., “Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia”, Environmental Monitoring and Assessment, 191(64): 1-12, (2019).
  • [9] Wadi, S.A., Alsaraireh, A.A., “Industrial data forecasting using discrete wavelet transform”, Italian Journal of Pure and Applied Mathematics. 40: 607-614, (2018).
  • [10] Md-Khair, N.Q.N., Samsudin, R., Shabri, A., “Forecasting crude oil prices using discrete wavelet transform with autoregressive integrated moving average and least square support vector machine combination approach”, International Journal on Advanced Science, Engineering and Information Technology. 7(4-2): 1553-1561, (2017).
  • [11] Zhu, L., Wang, Y., Fan, Q., “MODWT-ARMA model for time series prediction”, Applied Mathematical Modelling, 38: 1859-1865, (2014).
  • [12] Kalteh, A.M., “Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform”, Computers and Geosciences, 54: 1–8, (2013).
  • [13] Belayneh, A., Adamowski, J., Khalil, B., Quilty, J., “Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models”, Journal of Hydrolgy, 54: 1-8, (2014). [14] Lahmiri, S., “Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks”, Journal of King Saud University – Computer and Information Sciences”, 26: 218-227, (2014).
  • [15] Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J., “Artificial neural network forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation”, Atmospheric Environment 107: 118-128, (2015).
  • [16] Pradhan, P.P., Subudhi, P.P., “Wind speed forecasting based on wavelet transformation and recurrent neural network”, International Journal of Numerical Modelling, (2019). DOI: https://doi.org/10.1002/jnm.2670
  • [17] Seo, Y., Kim, S., Kisi, O., Singh, V., “Daily water level forecasting using wavelet decomposition and artificial intelligence techniques”, Journal of Hydrology, 520: 224-243, (2015).
  • [18] Parmar K., Bhardwaj, R., “River waver prediction modeling using neural networks, fuzzy and wavelet coupled model”, Water Resources Management, 29: 17-33, (2014).
  • [19] Seo, Y., Choi, Y., Choi, J., “River stage modeling by combining maximal overlap discrete wavelet transform, support vector machines and genetic algorithm”, Water, 9(7): 525, (2017).
  • [20] Yaslan, Y., Bican, B., “Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting”, Measurement, 103: 52-61, (2017).
  • [21] Liu, Z., Liu, J., “A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps”, Knowledge-Based Systems, 203: 106105, (2020).
  • [22] Yang, H-F., Chen, Y-P. P., “Hybrid deep learning and empirical model decomposition for tiem series applications”, Expert Systems and Applications, 120: 128-138, (2019).
  • [23] Yang, H-F., Chen, Y-P. P., “Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods”, Artificial Intelligence, 277: 103176, (2019).
  • [24] Chen, M-Y., Chen, B-T., “Online fuzzy time series analysis based on entropy discretization and fast fourier transform”, Applied Soft Computing, 14(B): 156-166, (2014).
  • [25] Halliday, J.R., Dorrell, D.G., Wood R.A., “An application of the fast fourier transform to the short-term prediction of sea wave behavior”, Renewable Enegry, 36(6): 1685-1692, (2011).
  • [26] Basakın, E.E., Ekmekcioğlu, Ö., Özger M., Çelik, A., “Prediction of Turkey wheat yield by wavelet fuzzy time series and gray prediction methods”, Türkiye Tarımsal Araştırmalar Dergisi, 7(3): 246-252, (2020).
  • [27] Başakın, E.E., Özger, M., “Montly river discharge prediction by wavelet fuzzy time series method”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(1): 17-35, (2021).
  • [28] Chen, C.C., Tsui, F.R., “Comparing different wavelet transform on removing electrocardiogram baseline wanders and special trends”, BMC Medical Informatics and Decision Making, 343, (2020).
  • [29] Özel, P., Akan, A., Yılmaz, B., “Noise-Assisted Multivariate Empirical Mode Decompotision based Emotion Recognition”, Electrica, 18(2): 263-274, (2018).
  • [30] Song, Q., Chissom, B.S., “Fuzzy time series and its models”, Fuzzy Set Systems, 54: 269-277, (1993a).
  • [31] Song, Q., Chissom, B.S., “Forecasting enrollments with fuzzy time series-Part I”, Fuzzy Set Systems 54: 1-9, (1993b).
  • [32] Song, Q., Chissom, B.S., “Forecasting enrollments with fuzzy time series-Part II”, Fuzzy Set Systems, 62: 1-8, (1994).
  • [33] Li, S. T., Cheng, Y. C., Lin, S. Y., “A FCM-Based deterministic forecasting model for fuzzy time series”, Computers and Mathematics with Applications, 56: 3052–3063, (2008).
  • [34] Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., Erilli, N. A., “Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering”, Expert Systems with Applications, 38: 10355-10357, (2011). [35] Guler Dincer, N., Akkus, O., “A new fuzzy clustering based on robust clustering for forecasting of air pollution”, Ecological Informatics, 43: 157-164, (2018).
  • [36] Gustafson, E., Kessel, W., “Fuzzy clustering with a fuzzy covariance matrix”, IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, San Diego, USA, (1979).
  • [37] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, (1981).
  • [38] Krishnapuram, R., Joshi, A., Yi, L., “A Fuzzy relative of the k-medoids algorithm with application to document and snippet clustering”, Proceedings IEEE International Conference on Fuzzy Systems. Seoul, South Korea, (1999).
  • [39] Percival, D.B., Walden A.T., ”Wavelet Methods for Time Series Analysis”, Cambridge University Press, (2000).
  • [40] Elayouty, A.S.M., “Time and frequency domain statistical methods for high-frequency time series”, PhD thesis, University of Glasgow, (2017).
  • [41] https://faculty.washington.edu/dbp/s530/PDFs/05-MODWT-2018.pdf. Access Date: 12.08.2020 [42] Makridakis, S.G., Wheelwright, S.C., Hyndman, R.J., Forecasting: Methods and Applications, John Wiley & Sons: New York, (1998).
  • [43] https://datamarket.com/data/license/0/default-open-license.html. Access Date: 12.08.2020
  • [44] https://machinelearningmastery.com/time-series-datasets-for-machine-learning/. Access Date: 13.08.2020
  • [45] https://www.kaggle.com. Access Date: 12.08.2020

A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform

Yıl 2022, Cilt: 35 Sayı: 3, 1152 - 1169, 01.09.2022
https://doi.org/10.35378/gujs.798423

Öz

This study proposes a new time series prediction method that combines Fuzzy Time Series (FTS) based on fuzzy clustering and Maximal Overlap Discrete Wavelet Transform (MODWT). Time series generally consist of subseries, each of which reflects the different behavior of the time series and using of a single prediction method for all subseries can be negatively impacted the prediction and forecasting accuracy. Proposed method is based on decomposing of time series into sub-time series through MODWT and predicting an FTS model for each sub-time series separately. Besides, time series can contain noise, outlier or unwanted data points and these points can hide the actual behavior of the time series. MODWT has the ability of eliminating negative effects of these kind of data points on the predictions. Besides, proposed method has also all advantages of FTS methods. The main objective of this study based on these advantages is to improve the prediction and forecasting performance of existing FTS methods based on fuzzy clustering. In order to show the performance of proposed method, three FTS methods based on fuzzy clustering and wavelet-based versions of them are applied to eight real time series and experimental results clearly showed that proposed method achieves the best prediction and forecasting results.

Kaynakça

  • [1] Box, G.E.P., Jenkins, G.M., Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, USA, (1970).
  • [2] Topuz, B.K., Bozoglu, M., Baser, U., Eroglu, N. A., “Forecasting of apricot production of Turkey by using Box-Jenkins method”, Turkish Journal of Forecasting. 2(2): 20-26, (2018).
  • [3] Mithiya, D., Datta, L., Mandal, K., “Time series analysis and forecasting of oilseeds production in India: using autoregressive integrated moving average and group method of data handling – neural network”, Asian Journal of Agricultural Extension, Economics & Sociology, 30(2): 1-14, (2019).
  • [4] Galeshchuk, S., “Neural Networks performance in exchange rate prediction”, Neurocomputing. 172: 446-452, (2016).
  • [5] Bas, E., Egrioglu, E., Aladag, C.H., Yolcu, U., “Fuzzy time series network used to forecast linear and nonlinear time series”, Applied Intelligence, 43: 343-355, (2015).
  • [6] Akdeniz, E., Egrioglu, E., Bas, E., Yolcu, U., “An ARMA type pi-sigma artificial neural network for nonlinear time series forecasting”, Journal of Artificial Intelligence and Soft Computing Research, 8(2): 121-132, (2018).
  • [7] Jiang, P., Dong, Q., Li, P.,“A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction”, Applied Soft Computing, 55: 44-62, (2017).
  • [8] Yong, N.K., Awang, N., “Wavelet-based time series model to improve the forecast accuracy of PM10 concentrations in Peninsular Malaysia”, Environmental Monitoring and Assessment, 191(64): 1-12, (2019).
  • [9] Wadi, S.A., Alsaraireh, A.A., “Industrial data forecasting using discrete wavelet transform”, Italian Journal of Pure and Applied Mathematics. 40: 607-614, (2018).
  • [10] Md-Khair, N.Q.N., Samsudin, R., Shabri, A., “Forecasting crude oil prices using discrete wavelet transform with autoregressive integrated moving average and least square support vector machine combination approach”, International Journal on Advanced Science, Engineering and Information Technology. 7(4-2): 1553-1561, (2017).
  • [11] Zhu, L., Wang, Y., Fan, Q., “MODWT-ARMA model for time series prediction”, Applied Mathematical Modelling, 38: 1859-1865, (2014).
  • [12] Kalteh, A.M., “Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform”, Computers and Geosciences, 54: 1–8, (2013).
  • [13] Belayneh, A., Adamowski, J., Khalil, B., Quilty, J., “Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models”, Journal of Hydrolgy, 54: 1-8, (2014). [14] Lahmiri, S., “Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks”, Journal of King Saud University – Computer and Information Sciences”, 26: 218-227, (2014).
  • [15] Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J., “Artificial neural network forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation”, Atmospheric Environment 107: 118-128, (2015).
  • [16] Pradhan, P.P., Subudhi, P.P., “Wind speed forecasting based on wavelet transformation and recurrent neural network”, International Journal of Numerical Modelling, (2019). DOI: https://doi.org/10.1002/jnm.2670
  • [17] Seo, Y., Kim, S., Kisi, O., Singh, V., “Daily water level forecasting using wavelet decomposition and artificial intelligence techniques”, Journal of Hydrology, 520: 224-243, (2015).
  • [18] Parmar K., Bhardwaj, R., “River waver prediction modeling using neural networks, fuzzy and wavelet coupled model”, Water Resources Management, 29: 17-33, (2014).
  • [19] Seo, Y., Choi, Y., Choi, J., “River stage modeling by combining maximal overlap discrete wavelet transform, support vector machines and genetic algorithm”, Water, 9(7): 525, (2017).
  • [20] Yaslan, Y., Bican, B., “Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting”, Measurement, 103: 52-61, (2017).
  • [21] Liu, Z., Liu, J., “A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps”, Knowledge-Based Systems, 203: 106105, (2020).
  • [22] Yang, H-F., Chen, Y-P. P., “Hybrid deep learning and empirical model decomposition for tiem series applications”, Expert Systems and Applications, 120: 128-138, (2019).
  • [23] Yang, H-F., Chen, Y-P. P., “Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods”, Artificial Intelligence, 277: 103176, (2019).
  • [24] Chen, M-Y., Chen, B-T., “Online fuzzy time series analysis based on entropy discretization and fast fourier transform”, Applied Soft Computing, 14(B): 156-166, (2014).
  • [25] Halliday, J.R., Dorrell, D.G., Wood R.A., “An application of the fast fourier transform to the short-term prediction of sea wave behavior”, Renewable Enegry, 36(6): 1685-1692, (2011).
  • [26] Basakın, E.E., Ekmekcioğlu, Ö., Özger M., Çelik, A., “Prediction of Turkey wheat yield by wavelet fuzzy time series and gray prediction methods”, Türkiye Tarımsal Araştırmalar Dergisi, 7(3): 246-252, (2020).
  • [27] Başakın, E.E., Özger, M., “Montly river discharge prediction by wavelet fuzzy time series method”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(1): 17-35, (2021).
  • [28] Chen, C.C., Tsui, F.R., “Comparing different wavelet transform on removing electrocardiogram baseline wanders and special trends”, BMC Medical Informatics and Decision Making, 343, (2020).
  • [29] Özel, P., Akan, A., Yılmaz, B., “Noise-Assisted Multivariate Empirical Mode Decompotision based Emotion Recognition”, Electrica, 18(2): 263-274, (2018).
  • [30] Song, Q., Chissom, B.S., “Fuzzy time series and its models”, Fuzzy Set Systems, 54: 269-277, (1993a).
  • [31] Song, Q., Chissom, B.S., “Forecasting enrollments with fuzzy time series-Part I”, Fuzzy Set Systems 54: 1-9, (1993b).
  • [32] Song, Q., Chissom, B.S., “Forecasting enrollments with fuzzy time series-Part II”, Fuzzy Set Systems, 62: 1-8, (1994).
  • [33] Li, S. T., Cheng, Y. C., Lin, S. Y., “A FCM-Based deterministic forecasting model for fuzzy time series”, Computers and Mathematics with Applications, 56: 3052–3063, (2008).
  • [34] Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., Erilli, N. A., “Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering”, Expert Systems with Applications, 38: 10355-10357, (2011). [35] Guler Dincer, N., Akkus, O., “A new fuzzy clustering based on robust clustering for forecasting of air pollution”, Ecological Informatics, 43: 157-164, (2018).
  • [36] Gustafson, E., Kessel, W., “Fuzzy clustering with a fuzzy covariance matrix”, IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, San Diego, USA, (1979).
  • [37] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, (1981).
  • [38] Krishnapuram, R., Joshi, A., Yi, L., “A Fuzzy relative of the k-medoids algorithm with application to document and snippet clustering”, Proceedings IEEE International Conference on Fuzzy Systems. Seoul, South Korea, (1999).
  • [39] Percival, D.B., Walden A.T., ”Wavelet Methods for Time Series Analysis”, Cambridge University Press, (2000).
  • [40] Elayouty, A.S.M., “Time and frequency domain statistical methods for high-frequency time series”, PhD thesis, University of Glasgow, (2017).
  • [41] https://faculty.washington.edu/dbp/s530/PDFs/05-MODWT-2018.pdf. Access Date: 12.08.2020 [42] Makridakis, S.G., Wheelwright, S.C., Hyndman, R.J., Forecasting: Methods and Applications, John Wiley & Sons: New York, (1998).
  • [43] https://datamarket.com/data/license/0/default-open-license.html. Access Date: 12.08.2020
  • [44] https://machinelearningmastery.com/time-series-datasets-for-machine-learning/. Access Date: 13.08.2020
  • [45] https://www.kaggle.com. Access Date: 12.08.2020
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Statistics
Yazarlar

Nevin Güler Dincer 0000-0003-0361-1803

Muhammet Oğuzhan Yalçın 0000-0003-4017-5588

Öznur İşçi Güneri 0000-0003-3677-7121

Yayımlanma Tarihi 1 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 35 Sayı: 3

Kaynak Göster

APA Güler Dincer, N., Yalçın, M. O., & İşçi Güneri, Ö. (2022). A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform. Gazi University Journal of Science, 35(3), 1152-1169. https://doi.org/10.35378/gujs.798423
AMA Güler Dincer N, Yalçın MO, İşçi Güneri Ö. A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform. Gazi University Journal of Science. Eylül 2022;35(3):1152-1169. doi:10.35378/gujs.798423
Chicago Güler Dincer, Nevin, Muhammet Oğuzhan Yalçın, ve Öznur İşçi Güneri. “A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform”. Gazi University Journal of Science 35, sy. 3 (Eylül 2022): 1152-69. https://doi.org/10.35378/gujs.798423.
EndNote Güler Dincer N, Yalçın MO, İşçi Güneri Ö (01 Eylül 2022) A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform. Gazi University Journal of Science 35 3 1152–1169.
IEEE N. Güler Dincer, M. O. Yalçın, ve Ö. İşçi Güneri, “A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform”, Gazi University Journal of Science, c. 35, sy. 3, ss. 1152–1169, 2022, doi: 10.35378/gujs.798423.
ISNAD Güler Dincer, Nevin vd. “A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform”. Gazi University Journal of Science 35/3 (Eylül 2022), 1152-1169. https://doi.org/10.35378/gujs.798423.
JAMA Güler Dincer N, Yalçın MO, İşçi Güneri Ö. A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform. Gazi University Journal of Science. 2022;35:1152–1169.
MLA Güler Dincer, Nevin vd. “A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform”. Gazi University Journal of Science, c. 35, sy. 3, 2022, ss. 1152-69, doi:10.35378/gujs.798423.
Vancouver Güler Dincer N, Yalçın MO, İşçi Güneri Ö. A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform. Gazi University Journal of Science. 2022;35(3):1152-69.