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Is it possible to accurately forecast the evolution of Brent crude oil prices? : an answer based on parametric and nonparametric forecasting methods

Álvarez-Díaz, Marcos - In: Empirical economics : a journal of the Institute for … 59 (2020) 3, pp. 1285-1305

Persistent link: https://www.econbiz.de/10012285558

Forecasting and Forecast-Combining of Quarterly Earnings-per-Share via Genetic Programming

Rodriguez, Arturo - 2013

Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the … models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are … traditional methodologies in either forecasting or forecast combining of quarterly earnings …

Persistent link: https://www.econbiz.de/10013074243

Using survey data to forecast real activity with evolutionary algorithms : a cross-country analysis

Claveria, Oscar; Monte, Enric; Torra, Salvador - In: Journal of applied economics 20 (2017) 2, pp. 329-349

Persistent link: https://www.econbiz.de/10011950622

Evolutionary computation for macroeconomic forecasting

Claveria, Oscar; Monte, Enric; Torra, Salvador - In: Computational economics 53 (2019) 2, pp. 833-849

Persistent link: https://www.econbiz.de/10012134879

Russia's comparative advantages in foreign trade : forecasting potential dynamics and identifying stimulation measures

Savin, Ivan - 2013

Persistent link: https://www.econbiz.de/10009684693

Optimised hybrid CNN bi-LSTM model for stock price forecasting

Patnaik, Deepti; Rao, N. V. Jagannadha; Padhiari, … - In: International journal of intelligent enterprise 11 (2024) 3, pp. 248-273

Persistent link: https://www.econbiz.de/10015065340

Extreme learning with chemical reaction optimization for stock volatility prediction

Nayak, Sarat; Mishra, Bijan Bihari - In: Financial innovation : FIN 6 (2020) 16, pp. 1-23

Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network...

Persistent link: https://www.econbiz.de/10012268745

Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization

Candelieri, Antonio; Giordani, Ilaria; Archetti, Francesco - In: Computers & operations research : and their … 106 (2019), pp. 202-209

Persistent link: https://www.econbiz.de/10012014411

Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine

Lu, Hongfang; Azimi, Mohammadamin; Iseley, Tom - In: Energy reports 5 (2019), pp. 666-677

(FFOA), simulated annealing algorithm (SA), cross factor (CF) and support vector machine (SVM) to forecast the short …

Persistent link: https://www.econbiz.de/10012020062

Dynamic stochastic lot sizing with forecast evolution in rolling-horizon planning

Forel, Alexandre; Grunow, Martin - In: Production and operations management : the flagship … 32 (2023) 2, pp. 449-468

Persistent link: https://www.econbiz.de/10014266046