Search Results - subject:"Hyperparameter"

Deep dive into churn prediction in the banking sector : the challenge of hyperparameter selection and imbalanced learning

Gkonis, Vasileios; Tsakalos, Ioannis - In: Journal of forecasting 44 (2025) 2, pp. 281-296

Persistent link: https://www.econbiz.de/10015374022

A novel hybrid deep learning method for accurate exchange rate prediction

Iqbal, Farhat; Koutmos, Dimitrios; Zaki, Eman Ahmed … - In: Risks : open access journal 12 (2024) 9, pp. 1-20

The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently,...

Persistent link: https://www.econbiz.de/10015066311

Using sequential statistical tests for efficient hyperparameter tuning

Buczak, Philip; Groll, Andreas; Pauly, Markus; Rehof, Jakob - In: AStA Advances in Statistical Analysis 108 (2024) 2, pp. 441-460

Hyperparameter tuning is one of the most time-consuming parts in machine learning. Despite the existence of modern … hyperparameter settings could be discarded after less than k resampling iterations if they are clearly inferior to high … underscore the potential for integrating sequential tests into hyperparameter tuning. …

Persistent link: https://www.econbiz.de/10015361330

Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises

Cruz, Yarens J.; Villalonga, Alberto; Castaño, Fernando; … - In: Operations research perspectives 12 (2024), pp. 1-10

Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in...

Persistent link: https://www.econbiz.de/10015055720

A transparent alternative to neural networks with an application to predicting volatility

Czasonis, Megan; Kritzman, Mark; Turkington, David - 2024 - This version: September 12, 2024

Persistent link: https://www.econbiz.de/10015101048

A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks

Raiaan, Mohaimenul Azam Khan; Sakib, Sadman; Fahad, Nur … - In: Decision analytics journal 11 (2024), pp. 1-32

advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time … hyperparameters. Our research offers an exhaustive categorization of these hyperparameter optimization (HPO) algorithms and … hyperparameter optimization. …

Persistent link: https://www.econbiz.de/10015101999

Predicting machine failures using machine learning and deep learning algorithms

Yadav, Devendra K.; Kaushik, Aditya; Yadav, Nidhi - In: Sustainable manufacturing and service economics 3 (2024), pp. 1-11

Industry 4.0 emphasizes real-time data analysis for understanding and optimizing physical processes. This study leverages a Predictive Maintenance Dataset from the UCI repository to predict machine failures and categorize them. This study covers two objectives namely, to compare the performance...

Persistent link: https://www.econbiz.de/10015332702

Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

Gerling, Alexander; Ziekow, Holger; Hess, Andreas; … - In: Journal of Intelligent Manufacturing 33 (2022) 2, pp. 555-573

RandomSearchCV and HyperOpt hyperparameter tuning. The algorithms are optimized based on multiple metrics, which we will introduce in …

Persistent link: https://www.econbiz.de/10015166371

The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index

Thi Thu Giang Nguyen; Ślepaczuk, Robert - 2022

Persistent link: https://www.econbiz.de/10013474013

Bilevel hyperparameter optimization for support vector classification : theoretical analysis and a solution method

Li, Qingna; Li, Zhen; Zemkoho, Alain B. - In: Mathematical methods of operations research : ZOR 96 (2022) 3, pp. 315-350

Persistent link: https://www.econbiz.de/10013455088