A nonlinear time-series prediction methodology based on neural networks and tracking signals
Natália Maria Puggina Bianchesi; Cláudia Eliane da Matta; Simone Carneiro Streitenberger; Estevão Luiz Romão; Pedro Paulo Balestrassi; Antônio Fernando Branco Costa
Abstract
Keywords
References
Aizenberg, I., Sheremetov, L., Villa-Vargas, L., & Martinez-Muñoz, J. (2016). Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production.
Amdoun, R., Khelifi, L., Khelifi-Slaoui, M., Amroune, S., Asch, M., Assaf-Ducrocq, C., & Gontier, E. (2018). The Desirability optimization methodology;a tool to predict two antagonist responses in biotechnological systems: case of biomass growth and hyoscyamine content in elicited datura starmonium hairy roots.
Amiri, E. (2015). Forecasting daily river flows using nonlinear time series models.
Armstrong, J. (2001).
Balestrassi, P., Popova, E., Paiva, A., & Lima, J. (2009). Design of experiments on neural network’s training for nonlinear time series forecasting.
Bandeira, S. G., Alcalá, S. G. S., Vita, R. O., & Barbosa, T. A. (2020). Comparison of selection and combination strategies for demand forecasting methods.
Berry, M., & Linoff, G. (1997).
Bianchesi, N., Romão, E., Lopes, M., Balestrassi, P., & Paiva, A. (2019). A design of experiments comparative study on clustering methods.
Bischak, D. P., & Trietsch, D. (2007). The rate of false signals in X̅ control charts with estimated limits.
Box, G. E. P., & Jenkins, G. M. (1970).
Brasil, Conselho Nacional do Meio Ambiente – CONAMA. (2007, August 9).
Brence, J., & Mastrangelo, C. (2006). Parameter selection for a robust tracking signal.
Brown, G. (1959).
Candioti, L. V., De Zan, M., Cámara, M., & Goicoechea, H. (2014). Experimental design and multiple response optimization: using the desirability function in analytical methods development.
Chan, K. S., & Tong, H. (1986). On estimating thresholds in autoregressive models.
Chang, J., & Tseng, C. (2017). Analysis of correlation between secondary PM2.5 and factory pollution sources by using ANN and the correlation coefficient.
Chen, T., & Chen, H. (1995). Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.
Cohen, J. (1988).
Corzo, G., & Solomatine, D. (2007). Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.
Cui, L., Wang, Z., & Zhou, X. (2012). Optimization of elicitors and precursors to enhance valtrate production in adventitious roots of Valeriana amurensis Smir. ex Kom.
Dascalescu, L., Medles, K., Das, S., Younes, M., Caliap, L., & Mihalcioiu, A. (2008). Using design of experiments and virtual instrumentation to evaluate the tribocharging of pulverulent materials in compressedair devices.
De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting.
Deboeck, G. J. (1994).
Deng, Y., Jaraiedi, M., & Iskander, W. (2004). Tracking signal test to monitor an intelligent time series forecasting model.
Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables.
Gardner Junior, E. (1985). CUSUM vs. smoothed error forecast monitoring schemes: some simulation results.
Granger, C. W. J., & Anderson, A. P. (1978).
Hamilton, J. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle.
Harrington, E. (1965). The desirability function.
Haykin, S. (2009).
Hippert, H. S., & Taylor, J. (2010). An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.
Hornik, K. (1993). Some new results on neural network approximation.
Hu, J., Wang, X., Zhang, Y., Zhang, D., Zhang, M., & Xue, J. (2020). Time series prediction method based on variant LSTM recurrent neural network.
Igunnu, E. T., & Chen, G. Z. (2014). Produced water treatment technologies.
Kialashaki, A., & Reisel, J. (2013). Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks.
Krishnamurthy, B. (2006).
Kumar, D., & Murugan, S. (2017). A novel fuzzy time series model for stock market index analysis using neural network with tracking signal approach.
Lee, J. Y., Chang, J. H., Kang, D. H., Kim, S. I., & Hong, J. P. (2007). Tooth shape optimization for cogging torque reduction of transverse flux rotary motor using design of experiment and response surface methodology.
Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021). Forecast methods for time series data: a survey.
Lorscheid, I., Heine, B. O., & Meyer, M. (2012). Opening the ‘black box’ of simulations: Increased transparency and effective communication through the systematic design of experiments.
Makridakis, S., & Wheelwright, S. (1989).
Mao, S., & Xiao, F. (2019). Time series forecasting based on complex network analysis.
Matta, C., Bianchesi, N., Oliveira, M., Balestrassi, P., & Leal, F. (2021). A comparative study of forecasting methods using real-life econometric series data.
McClain, J. (1988). Dominant tracking signals.
Mircetic, D., Rostami-Tabar, B., Nikolicic, S., & Maslaric, M. (2022). Forecasting hierarchical time series in supply chains: an empirical investigation.
Mo, F., Shen, C., Zhou, J., & Khonsari, M. (2017). Statistical analysis of the influence of imperfect texture shape and dimensional uncertainty on surface texture performance.
Montgomery, C. D. (2009).
Olson, O., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction.
Pant, M., & Kumar, S. (2022). Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting.
Priestley, M. (1980). State-dependent models: a general approach to nonlinear time series analysis.
Qian, B., Xiao, Y., Zheng, Z., Zhou, M., Zhuang, W., Li, S., & Ma, Q. (2020). Dynamic multi-scale convolutional neural network for time series classification.
Ravi, P. (2014). An analysis of a widely used version of the CUSUM tracking signal.
Ray, J., & Engelhardt, F. R. (1992).
Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper and L. V. Hedges (Eds.),
Sabeti, H., Al-Shebeeb, O., & Jaridi, M. (2016). Forecasting system monitoring under non-normal input noise distributions.
Santos, C. H., Lima, R. D. C., Leal, F., Queiroz, J. A., Balestrassi, P. P., & Montevechi, J. A. B. (2020). A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods.
Sun, K., Huang, S., Wong, D., & Jang, S. (2017). Design and application of a variable selection method for multilayer perceptron neural network with LASSO.
Superville, C. (2019). Tracking signal performance in monitoring manufacturing processes.
Tong, H. (1978). On a threshold model. In C. H. Chen (Ed.),
Trigg, W. (1964). Monitoring a forecasting system.
Tsay, R. (2005).
Verma, P., Reddy, S. V., Ragha, L., & Datta, D. (2021). Comparison of time-series forecasting models. In
Wang, Z., & Lou, Y. (2019). Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM. In
Wong, W. K., Xia, M., & Chu, W. C. (2010). Adaptive neural network model for time-series forecasting.
Xiao, D., Shi, H., & Wu, D. (2012). Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm.
Xiao, H., Jiang, X., Chen, C., Wang, W., Wang, C., Ali, A., Berthe, A., Moussa, R., & Diaby, V. (2020). Using time series analysis to forecast the health-related quality of life of post-menopausal women with non-metastatic ER+ breast cancer: a tutorial and case study.
Yang, M. (2011). Measurement of oil in Produced Water. In K. Lee & J. Neff (Eds.),
Yu, L., & Lai, K. (2005). Adaptive smoothing neural networks in foreign exchange rate forecasting. In
Zhai, X., Ali, A., Amira, A., & Bensaali, F. (2016). MLP neural network based gas classification system on Zynq SoC.
Submitted date:
05/13/2022
Accepted date:
09/09/2022