Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting
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- Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
- William Lam & Y. Tang & K. Chan & Mei-Lam Tam, 2006. "Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census," Transportation, Springer, vol. 33(3), pages 291-310, May.
- Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
- Fan, Lei & Wilson, William W. & Dahl, Bruce, 2012. "Congestion, port expansion and spatial competition for US container imports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1121-1136.
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- Tutun, Salih & Chou, Chun-An & Canıyılmaz, Erdal, 2015. "A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey," Energy, Elsevier, vol. 93(P2), pages 2406-2422.
- Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
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KeywordsInspection forecasting; Artificial neural networks; SARIMA; Hybrid models;
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