Research on Multi-step Mixed Predictiom Model of Coal Gasifier Furnace Temperature Based on Machine Learning

Zhao, Yuchen; Ma, Zengwei; Han, Xuefeng

doi:10.1088/1742-6596/2187/1/012070

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…ere are many factors affecting stock market volatility, and research data are often difficult [14]. Machine learning has become an important method for forecasting research on Asian stock markets.…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Lee

Huang

Tang

2022

Security and Communication Networks

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With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence has developed comprehensively in different fields, and it has been widely used in the field of the financial economy. Machine learning under artificial intelligence is currently widely used in stock market volatility dynamics and related research. This paper applied machine learning to the prediction of the dynamic relationship of Asian stock market volatility and established a model for predicting the dynamic relationship of stock market volatility under machine learning. By using statistical theory, linear support vector machines, generalizable bounds, and other algorithms, it provides the theoretical basis and feasibility analysis for the model. Through investigation and research, this paper found that compared with ordinary forecasting model methods, the stock volatility dynamic trend forecasting model based on machine learning has a relatively complete forecasting effect, and the accuracy of the machine learning forecasting model was up to 52%. The lowest was 39%, the average prediction accuracy was 46.5%, and the accuracy was improved by 16.8%. This showed that the introduction of machine learning prediction models in the dynamic prediction model of Asian stock volatility is relatively successful.

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Section: Introductionmentioning

confidence: 99%

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Lee

Huang

Tang

2022

Security and Communication Networks

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Credit risk Prediction Model of Financial Companies Based on Machine Learning Algorithm

Wang

2023

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)

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A Multi-Step Furnace Temperature Prediction Model for Regenerative Aluminum Smelting Based on Reversible Instance Normalization-Convolutional Neural Network-Transformer

Dai,

Ling,

Shi

et al. 2024

Processes

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In the regenerative aluminum smelting process, the furnace temperature is critical for the quality and energy consumption of the product. However, the process requires protective sensors, making real-time furnace temperature measurement costly, while the strong nonlinearity and distribution drift of the process data affect furnace temperature prediction. To handle these issues, a multi-step prediction model for furnace temperature that incorporates reversible instance normalization (RevIN), convolutional neural network (CNN), and Transformer is proposed. First, the self-attention mechanism of the Transformer is combined with CNN to extract global and local information in the furnace temperature data, thus addressing the strong nonlinear characteristics of the furnace temperature. Second, RevIN with learnable affine transformation is utilized to address the distribution drift in the furnace temperature data. Third, the temporal correlation of the prediction model is enhanced by a time-coding method. The experimental results show that the proposed model demonstrates higher prediction accuracy for furnace temperature at different prediction steps in the regenerative aluminum smelting process compared to other models.

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Research on Multi-step Mixed Predictiom Model of Coal Gasifier Furnace Temperature Based on Machine Learning

Cited by 3 publications

References 3 publications

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Credit risk Prediction Model of Financial Companies Based on Machine Learning Algorithm

A Multi-Step Furnace Temperature Prediction Model for Regenerative Aluminum Smelting Based on Reversible Instance Normalization-Convolutional Neural Network-Transformer

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