Early Warning of Telecom Customer Churn Based on Multialgorithm Model Optimization

Xu, Jingxiu; Li, Xueguang; He, Zhonglin; Zhou, Jing

doi:10.3389/fenrg.2022.946933

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…It can run entirely on electricity. Its main advantages are no use of fossil energy and no polluting automobile exhaust [5]. This is of great significance for environmental protection and reducing the consumption of oil resources [6].…”

Section: Introductionmentioning

confidence: 99%

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Li,

Dumlao

2023

FCIS

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New energy vehicles are developing rapidly in the world, China and Europe are vigorously promoting new energy vehicles. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM-Attention-R network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The Attention algorithm is used to extract the global information of features and solve the problem of long-term dependency. To ensure the diversity of feature extraction, the Attention algorithm in this paper uses multi-headed self-attentiveness. The CACLE dataset from the University of Maryland is used in this paper. Through the training of the model and the comparison, it is concluded that the LSTM-Attention-R algorithm networks proposed in this article can predict the value of SOC well. Meanwhile, this paper compares the LSTM-Attention-R algorithm with the LSTM algorithm, and also compares the LSTM-Attention-R algorithm with the Attention algorithm. Finally, it is concluded that the accomplishment of the network framework contrived is superior to the performance of these two algorithms alone. Finally, the algorithm has good engineering practice implications. The algorithm proposed provides a better research direction for future parameter prediction in the field of lithium batteries. It has a better theoretical significance.

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Section: Introductionmentioning

confidence: 99%

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Li,

Dumlao

2023

FCIS

Self Cite

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Predict customer churn using combination deep learning networks model

2023

Neural Comput & Applic

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Customer Churn in Subscription Business Model—Predictive Analytics on Customer Churn

Zhang¹

2023

BCPBM

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There is a growing tendency for more companies to develop towards a subscription business model. Under such a trend, it is important to learn about the customer churn rate within the business, learn from it and adjust business strategies accordingly. This paper aims to predict customer churn rate in subscription business models using a variety of machine learning algorithms. Through comparing the results from the different algorithms, the best algorithms can be identified so that it provides an insight on which algorithm a subscription business should choose in order to predict customer churn most effectively. In this work, a total of 21 features and 9 algorithms are taken into account. Through a set of rigorous procedure including data preparation, feature engineering, feature selection, model building, and finally, model evaluation, three algorithms, namely Logistic Regression, Gradient Boosting (SMOTE) and Neural Network outperformed other 6 algorithms. The best performing algorithm being Logistic Regression with its 79.6% prediction accuracy, thus the conclusion that when subscription business predicts customer churn rate, Logistic Regression is the most preferable algorithm. During the process of feature engineering, SMOTE did not improve the model performance as it supposed to, so it is not recommended during the model building process.

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Early Warning of Telecom Customer Churn Based on Multialgorithm Model Optimization

Cited by 3 publications

References 16 publications

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Predict customer churn using combination deep learning networks model

Customer Churn in Subscription Business Model—Predictive Analytics on Customer Churn

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