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Research Article

Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network

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Pages 383-404 | Received 31 May 2018, Accepted 10 Mar 2019, Published online: 08 Apr 2019

References

  • Aladag, C. H., Egrioglu, E., & Kadilar, C. (2009). Forecasting nonlinear time series with a hybrid methodology. Applied Mathematic Letters, 22, 1467–1470.
  • Apolloni, B., Bassis, S., Rota, J., Galliani, G. L., Gioia, M., & Ferrari, L. (2016). A neuro fuzzy algorithm for learning from complex granules. Granular Computing, 1(4), 225–246.
  • Aydin, D., & Mammadov, M. (2014). Time series forecasting using a hybrid neural networks and nonparametric regression model. Pakistan Journal of Statistics, 30(3), 309–322.
  • Bickel, P. J., Götze, F., & Zwet, W. (1997). Resampling fewer than n observations: Gains. loses and remedies for losses. Statistica Sinica, 7, 1–31.
  • BuHamra, S., Smaoui, N., & Gabr, M. (2003). The Box-Jenkins analysis and neural networks: Prediction and time series modeling. Applied Mathematical Modelling, 27, 805–815.
  • Bühlmann, P. (1997). Sieve bootstrap for time series. Bernoulli, 3(2), 123–148.
  • Cai, M., Li, Q., & Lang, G. (2017). Shadowed sets of dynamic fuzzy sets. Granular Computing, 2(2), 85–94.
  • Chen, S. M. (1996). A fuzzy reasoning approach for rule-based systems based on fuzzy logics. IEEE Transactions on Systems. Man and Cybernetics-Part B: Cybernetics, 26(5), 769–778.
  • Chen, S. M., & Chang, T. H. (2001). Finding multiple possible critical paths using fuzzy PERT. IEEE Transactions on Systems. Man. And Cybernetics - Part B: Cybernetics, 31(6), 930–937.
  • Chen, S. M., & Chen, C. D. (2011). Handling forecasting problems based on high-order fuzzy logical relationships. Expert Systems with Applications, 38(4), 3857–3864.
  • Chen, S. M., & Chung, N. Y. (2006). Forecasting enrollments using high-order fuzzy time series and genetic algorithms. International Journal of Intelligent Systems, 21(5), 485–501.
  • Chen, S. M., & Kao, P. Y. (2013). TAIEX forecasting based on fuzzy time series. particle swarm optimization techniques and support vector machines. Information Sciences, 247, 62–71.
  • Chen, S. M., Wang, N. Y., & Pan, J. S. (2009). Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Systems with Applications, 36(8), 11070–11076.
  • Cheng, C. T., Niu, W. J., Feng, Z. K., Shen, J. J., & Chau, K. W. (2015). Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water, 7, 4232–4246.
  • Ciucci, D. (2016). Orthopairs and granular computing. Granular Computing, 1(3), 159–170.
  • Cogollo, M. R., & Velásquez, J. D. (2014). Methodological advances in artificial neural networks for time series forecasting. IEEE Latin America Transactions, 12, 764–771.
  • Deo, R. C., & Sahin, M. (2015). Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydro meteorological parameters and climate indices in eastern Australia. Atmospheric Research, 161–162, 65–81.
  • Dubois, D., & Prade, H. (2016). Bridging gaps between several forms of granular computing. Granular Computing, 1(2), 115–126.
  • Efron, B. (1979). Bootstrap methods: Another look at the Jackknife. The Annals of Statistics, 7(1), 1–26.
  • Fang, D., & Wang, J. A. (2017). Novel application of artificial neural network for wind speed estimation. International Journal of Sustainable Energy, 36(5), 415–429.
  • Germi, M. B., Mirjavadi, M., Namin, A. S. S., & Baziar, A. (2014). A hybrid model for daily peak load power forecasting based on SAMBA and neural network. Journal of Intelligent and Fuzzy Systems, 27(2), 913–920.
  • Glišović, N., Milenković, M., Bojović, N., Švadlenka, L., & Avramović, Z. (2016). A hybrid model for forecasting the volume of passenger flows on Serbian railways. Operational Research, 16(2), 271–285.
  • Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water Resources Management, 30, 375–391.
  • Hajizade, E., Mahootchi, M., Esfahanipour, A., & Massahi, K. M. (2015). A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility. Neural Computing and Applications.
  • Hall, P., & Jing, B. Y. (1996). On sample re-use methods for dependent data. Journal of the Royal Statistics Society. Series B, 58, 727–738.
  • Ince, H., & Traffalis, T. B. (2005). A hybrid model for exchange rate prediction. Decisions Support Systems, 42, 1054–1062.
  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks. Piscataway. 4, 1942-1948. doi: 10.1109/ICNN.1995.488968.
  • Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39, 4344–4357.
  • Khosravi, A., Nahavandi, S., Creighton, D., & Atiya, A. F. (2011). Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Transactions on Neural Networks, 22(3), 337–346.
  • Kim, M. K. (2015). A new approach to short-term price forecast strategy with an artificial neural network approach: Application to the Nord Pool. Journal of Electrical Engineering and Technology, 10(4), 1480–1491.
  • Kumar, M., & Thenmozhi, M. (2014). Forecasting stock index returns using ARIMA-SVM. ARIMA-ANN. and ARIMA-random forest hybrid models. International Journal of Banking. Accounting and Finance, 5(3), 284–308.
  • Künsch, H. R. (1989). The Jackknife and the bootstrap for general stationary observations. The Annals of Statistics, 17(3), 1217–1241.
  • Lahiri, S. N. (2003). Resampling Methods for Dependent Data. New York, NY: Springer-Verlag.
  • Lee, Y.-S., & Tong, L.-I. (2011). Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowledge-Based Systems, 24, 66–72.
  • Liu, R. Y., & Singh, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence. In R. LePage & L. Billard (Eds.), Exploring the Limits of Bootstrap. New York, NY: Wiley. pp. 225–248.
  • Livi, L., & Sadeghian, A. (2016). Granular computing. computational intelligence. and the analysis of non-geometric input spaces. Granular Computing, 1(1), 13–20.
  • Loia, V., D’Aniello, G., Gaeta, A., & Orciuoli, F. (2016). Enforcing situation awareness with granular computing: A systematic overview and new perspectives. Granular Computing, 1(2), 127–143.
  • Ma, Y., Jiang, C., Hou, Z., & Wang, C. (2006). The formulation of the optimal strategies for the electricity producers based on the particle swarm optimization algorithm. IEEE Transactions on Power Systems, 21(4), 1663–1671.
  • Maciel, L., Ballini, R., & Gomide, F. (2016). Evolving granular analytics for interval time series forecasting. Granular Computing, 1(4), 213–224.
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
  • Pandhiani, M., Muhammed, S., & Bin Shabri, A. (2015). Time series forecasting by using hybrid models for monthly streamflow data. Applied Mathematical Sciences, 9, 2809–2829.
  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162–2172.
  • Pereira, E. N., Scarpin, C. T., & Teixeira Júnior, L. A. (2015). Hybrid wavelet model for time series prediction. Applied Mathematical Sciences, 9, 7431–7438.
  • Peters, G., & Weber, R. (2016). DCC: A framework for dynamic granular clustering. Granular Computing, 1(1), 1–11.
  • Piri, J., Mohammadi, K., Shamshirband, S., & Akib, S. (2016). Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation. Environmental Earth Sciences, 75(3), art. no. 246, 1–13.
  • Politis, D. N., & Romano, J. P. (1994). Large sample confidence regions based on subsamples under minimal assumptions. The Annals of Statistics, 22, 2031–2050.
  • Qin, S., Wang, J., Wu, J., & Zhao, G. (2016). A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China. International Journal of Green Energy, 13(6), 595–607.
  • Sanchez, M. A., Castro, J. R., Castillo, O., & Mendoza, O. (2017). Fuzzy higher type information granules from an uncertainty measurement. Granular Computing, 2(2), 95–103.
  • Shafaei, M., Adamowski, J., Fakheri-Fard, A., Dinpashoh, Y., & Adamowski, K. A. (2016). Wavelet- SARIMA-ANN hybrid model for precipitation forecasting. Journal of Water and Land Development, 28(1), 27–36.
  • Shao, Y. E., Hou, C.-D., & Lin, C.-S. (2015). Applying artificial neural networks. multiple regression and their hybrid models for predictions of electricity sales in Taiwan. ICIC Express Letters. Part B: Applications, 6(2), 485–490.
  • Shi, Y., & Eberhart, R. C. (1999, July). Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (pp. 1945–1950). Washington, D.C., USA.
  • Shukur, O. B., & Lee, M. H. (2015). Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA. Renewable Energy, 76, 637–647.
  • Singh, P. K., & Kumar, C. A. (2017). Concept lattice reduction using different subset of attributes as information granules. Granular Computing, 2(3), 159–173.
  • Skowron, A., Jankowski, A., & Dutta, S. (2016). Interactive granular computing. Granular Computing, 1(2), 95–113.
  • Štěpnička, M., Cortez, P., Donate, J. P., & Štěpničková, L. (2013). Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations. Expert Systems with Applications, 40(6), 1981–1992.
  • Syau, Y. R., Skowron, A., & Lin, E. B. (2017). Inclusion degree with variable-precision model in analyzing inconsistent decision tables. Granular Computing, 2(2), 65–72.
  • Tseng, F. M., Yu, H. C., & Tzeng, G. H. (2002). Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change, 69, 71–87.
  • Wang, G., Li, Y., & Li, X. (2017). Approximation performance of the nonlinear hybrid fuzzy system based on variable universe. Granular Computing, 2(2), 73–84.
  • Wang, G., Yang, J., & Xu, J. (2017). Granular computing: From granularity optimization to multi-granularity joint problem solving. Granular Computing, 2(3), 105–120.
  • Wilke, G., & Portmann, E. (2016). Granular computing as a basis of human-data interaction: A cognitive cities use case. Granular Computing, 1(3), 181–197.
  • Yao, Y. (2016). A triarchic theory of granular computing. Granular Computing, 1(2), 145–157.
  • Yolcu, U., Aladag, C. H., & Egrioglu, E. (2013). A new linear & nonlinear artificial neural network model for time series forecasting. Decision Support System Journals, 54, 1340–1347.
  • Yolcu, U., Jin, Y., & Egrioglu, E. (2016, December). An ensemble of single multiplicative neuron models for probabilistic prediction. 2016 IEEE Symposium Series on Computational Intelligence (art. no. 7849975). Athens, Greece.
  • Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.

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