Comparison of Multiple Machine Learning Models Based on Enterprise Revenue Forecasting

Huang, Lei; Cailan, Huang

doi:10.1109/acctcs52002.2021.00077

Cited by 7 publications

(11 citation statements)

References 13 publications

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“…have been used to analyze big data to predict a company's financial risk [33]. In [34], we select top-performing companies in 20 industries using the business revenue dataset to predict and model the relationship between features and business revenue by using random forests, gradient boosted regression trees, and support vector machines to solve the revenue forecasting problem of companies. And in [35], the honey industry requires simple, reliable and accurate analysis of honey adulterations to assess their purity for commercial purposes.…”

Section: Methodsmentioning

confidence: 99%

“…The predictive performance of the above machine learning method is then compared to stacked regression, a technique that integrates the performance of the various techniques described above. Overall [29][30][31][32][33][34][35], the literature shows that various machine learning techniques can predict other types of targets and compare accuracy levels.…”

Section: Methodsmentioning

confidence: 99%

“…The term F(xt) represents the target prediction model. Test samples are input into this model to obtain noise prediction results [27,29,30,34,35]. Algorithm is presented in the following text.…”

Section: A Gradient Boosting Modelmentioning

confidence: 99%

“…RF is a non-linear statistical ensemble regression technique that builds a large number of random decision trees and then averages them. There are three core elements in this algorithm, namely the functional model, objective function and optimization algorithm [29,31,33,34,35], are represented by the following text.…”

Section: B Random Forestmentioning

confidence: 99%

“…It is capable of classifying data for almost any problem, including linear and nonlinear problems. The essence of the algorithm is to find the optimal model with the largest interval [30,32,33,34,35]. Algorithm can be described as the following text.…”

Section: Support Vector Machinementioning

confidence: 99%

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Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Wen

Huang

2022

IEEE Access

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The effect of noise on the human body has attracted increasing research attention. In particular, many factories generate motor noise pollution, which exposes general workers to noise for extended periods. To solve this problem, masks made of different materials are used for reducing the noise generated by motors. In this study, we attempted to predict the acoustic sound of masked motors. We collected noise level data in decibels for different operation frequencies of motors used at National Synchrotron Radiation Research Center (NSRRC) and developed a machine learning model according to the characteristics of the collected data to simulate the effect of masks on the motor sound. We use the Gradient Boost Model (GBM) as the main learning method because the model is suitable for predicting noise from comparison results of the five models are very common predictive models and may performed as compare method to predict acoustic noise. The results indicated that the prediction accuracy of the GBM was considerably higher than other four traditional machine learning methods (random forests, support vector machine, gaussian processes regression model and multiple linear regression models). Moreover, we used a general multiple linear regression method as the worst method of comparison and conducted time-frequency visualization of the sound for analysis. At NSRRC, we examined the effects of three observation locations and three mask materials, namely wood, metal, and acrylic, on the sound prediction accuracy achieved with the developed model. The highest sound prediction accuracy was obtained behind the motor and under an acrylic mask.INDEX TERMS Gradient boosting model (GBM), machine learning, motor noise prediction, timefrequency diagrams.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Gradient Boosting Modelmentioning

confidence: 99%

Section: B Random Forestmentioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

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Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Wen

Huang

2022

IEEE Access

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Multivariate dynamic modeling for Bayesian forecasting of business revenue

Yanchenko

Tierney

Lawson

et al. 2022

Appl Stoch Models Bus & Ind

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Forecasting enterprise‐wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large‐scale retail company. Focused on multivariate revenue forecasting across collections of supermarkets and product categories, hierarchical dynamic models are natural: these are able to couple revenue streams in an integrated forecasting model, while allowing conditional decoupling to enable relevant and sensitive analysis together with scalable computation. Structured models exploit multi‐scale modeling to cascade information on price and promotion activities as predictors relevant across categories and groups of stores. With a context‐relevant focus on forecasting revenue 12 weeks ahead, the study highlights product categories that benefit from multi‐scale information, defines insights into when, how, and why multivariate models improve forecast accuracy, and shows how cross‐category dependencies can relate to promotion decisions in one category impacting others. Bayesian modeling developments underlying the case study are accessible in custom code for interested readers.

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