Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction
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- Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
- Bollerslev, Tim & Engle, Robert F, 1993. "Common Persistence in Conditional Variances," Econometrica, Econometric Society, vol. 61(1), pages 167-186, January.
- Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
- Abdullah H. Alenezy & Mohd Tahir Ismail & Sadam Al Wadi & Jamil J. Jaber, 2023. "Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models," Risks, MDPI, vol. 11(7), pages 1-16, July.
- Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Bollerslev, Tim, 2023. "Reprint of: Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics, Elsevier, vol. 234(S), pages 25-37.
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Keywords
optimization; dynamic systems; data modeling; forecasting; time series; fuzzy systems; soft computing; adaptive systems;All these keywords.
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