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Symbolic regression-based adaptive generation of implied volatility

Yen, Joseph; Qi, Yuan Yuan; Wong, Seng Fat; Zhou, Jiantao - In: International journal of financial engineering 9 (2022) 3, pp. 2250018-1-2250018-27

Persistent link: https://www.econbiz.de/10013367616

Energy ratio of produced biodiesel in hydrodynamic cavitation reactor equipped with LabVIEW controller and artificial intelligence

Naderloo, Leila - In: Energy reports 6 (2020), pp. 1456-1467

(ANFIS), artificial neural network (ANN) and response surface methodology (RSM) methods were employed. The average energy … ratio was obtained 1.205, and the R2 of the best ANFIS, ANN and RSM models were 0.989, 0.966, and 0.990, respectively, and … MSE was calculated at 0.0005, 0.0015 and 0.00003. The results revealed that the RSM and ANFIS models were preferred to the …

Persistent link: https://www.econbiz.de/10012266041

Sustainable development : an artificial intelligence approach

Duvnjak, Kristina; Gregoric, Maja; Gorše, Mateja - In: Management research and practice : MRP 12 (2020) 1, pp. 18-28

Persistent link: https://www.econbiz.de/10012415118

Sustainable development : an artificial intelligence approach

Duvnjak, Kristina; Gregoric, Maja; Gorše, Mateja - In: Management research and practice : MRP 12 (2020) 4, pp. 18-28

Persistent link: https://www.econbiz.de/10012415229

The chatbot artificial intelligence as the alternative customer services strategic to improve the customer relationship management in real-time responses

Sofiyah, Fivi Rahmatus; Dilham, Ami; Hutagalung, Arif Qaedi - In: International journal of economics and business … 27 (2024) 5, pp. 45-58

Persistent link: https://www.econbiz.de/10015063309

Machine Forecast Disagreement

Bali, Turan G.; Kelly, Bryan T.; Mörke, Mathis; … - 2023

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

Persistent link: https://www.econbiz.de/10014340974

Churn rate modeling for telecommunication operators using data science methods

Zatonatska, Tetiana; Fareniuk, Yana; Shpyrko, Viktor - In: Marketing i menedžment innovacij : m&mi 14 (2023) 2, pp. 163-173

Persistent link: https://www.econbiz.de/10014373841

An empirical validation protocol for large-scale agent-based models

Barde, Sylvian; Hoog, Sander van der - 2017

Despite recent advances in bringing agent-based models (ABMs) to the data, the estimation or calibration of model parameters remains a challenge, especially when it comes to large-scale agent-based macroeconomic models. Most methods, such as the method of simulated moments (MSM), require...

Persistent link: https://www.econbiz.de/10011689008

Agent-based model calibration using machine learning surrogates

Lamperti, Francesco; Roventini, Andrea; Sani, Amir - 2017

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and...

Persistent link: https://www.econbiz.de/10011630888

Agent-based model calibration using machine learning surrogates

Lamperti, Francesco; Roventini, Andrea; Sani, Amir - 2017

Persistent link: https://www.econbiz.de/10011708084