Deep learning and forecasting in practice: an alternative costs case

Zema, Tomasz; Kozina, Agata; Sulich, Adam; Römer, Ingolf; Schieck, Martin

doi:10.1016/j.procs.2022.09.354

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…This system outperforms single models and other ensemble models in terms of prediction accuracy and recall 37 . In the realm of forecasting leasing alternative costs, deep learning models have been applied and demonstrated smaller deviations between predicted opportunity costs and actual values, surpassing existing advanced models 38 . Additionally, researchers have conducted a comprehensive analysis of different graph neural network models in the context of stock prediction, paving the way for future advancements in this domain 39 …”

Section: Related Workmentioning

confidence: 99%

“…37 In the realm of forecasting leasing alternative costs, deep learning models have been applied and demonstrated smaller deviations between predicted opportunity costs and actual values, surpassing existing advanced models. 38 Additionally, researchers have conducted a comprehensive analysis of different graph neural network models in the context of stock prediction, paving the way for future advancements in this domain. 39 These research efforts demonstrate significant achievements in utilizing machine learning algorithms, stacking models, and deep learning methods in classification tasks across various domains.…”

Section: Deep Learning Modelmentioning

confidence: 99%

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Joint loan risk prediction based on deep learning‐optimized stacking model

Wang,

Pan

et al. 2023

Engineering Reports

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In recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

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Section: Related Workmentioning

confidence: 99%

Section: Deep Learning Modelmentioning

confidence: 99%

Joint loan risk prediction based on deep learning‐optimized stacking model

Wang,

Pan

et al. 2023

Engineering Reports

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“…The paper underscores the importance of AI in achieving sustainable intensification in agriculture, which is crucial for meeting the increasing global food demand while minimizing environmental impacts. The authors' findings suggest that AI-driven smart farming and precision agriculture can significantly contribute to the sustainability goals of modern agriculture (Zema i in., 2022).…”

Section: Introductionmentioning

confidence: 99%

Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023

Zema

2023

Self Cite

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This report provides an overview of the Invited Session titled "Computer Science for Green Technologies and Sustainable Development", held during the 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems from September 6–8, 2023, in Athens, Greece. The session was co-chaired by Adam Sulich and Tomasz Zema, with additional organizational support from Letycja Sołoducho-Pelc. The session requirement was attendance by participants in person. The purpose of this report is to summarize the papers presented and the discussions that took place within the session. Therefore, this paper has a descriptive approach and does not attempt to combine the presented papers.

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Model of Relationship Between Circular Economy and Industry 5.0

Sulich

2024

IFIP Advances in Information and Communication Technology

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Deep learning and forecasting in practice: an alternative costs case

Cited by 8 publications

References 13 publications

Joint loan risk prediction based on deep learning‐optimized stacking model

Joint loan risk prediction based on deep learning‐optimized stacking model

Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023

Model of Relationship Between Circular Economy and Industry 5.0

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