传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用...传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用户主动离网的主要原因是其他运营商推出新的营销策略这一事实,通过比较用户在不同营销策略下可能发生的行为,进而预测用户离网的可能性。实验结果表明,基于CPMP模型对不同营销方案比较所得出差异,直接影响用户离网率。通过对营销策略的对比分析,可有效控制离网率。展开更多
The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series ...The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series prediction models,including RNN,LSTM and their deformed bodies,show the problems of gradient disappearance and low efficiency in long-span prediction.This paper proposes a long-term and short-term memory network architecture,which based on Encoder and Decoder Stacks and self-attention mechanism,replacing the feature extraction part of traditionalLSTMthrough self-attentionmechanism and provides interpretable insights into the dynamics of time.Through the results of simulation experiments,this paper shows the comparison of stock prediction effects through using RNN,Bi-LSTM and Encoder and Decoder-Attention-LSTM models.The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model,and can achieve high accuracy when the epoch is small.展开更多
文摘传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用户主动离网的主要原因是其他运营商推出新的营销策略这一事实,通过比较用户在不同营销策略下可能发生的行为,进而预测用户离网的可能性。实验结果表明,基于CPMP模型对不同营销方案比较所得出差异,直接影响用户离网率。通过对营销策略的对比分析,可有效控制离网率。
基金This work is supported by the National Nature Science Foundation of China through project 51979048.
文摘The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series prediction models,including RNN,LSTM and their deformed bodies,show the problems of gradient disappearance and low efficiency in long-span prediction.This paper proposes a long-term and short-term memory network architecture,which based on Encoder and Decoder Stacks and self-attention mechanism,replacing the feature extraction part of traditionalLSTMthrough self-attentionmechanism and provides interpretable insights into the dynamics of time.Through the results of simulation experiments,this paper shows the comparison of stock prediction effects through using RNN,Bi-LSTM and Encoder and Decoder-Attention-LSTM models.The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model,and can achieve high accuracy when the epoch is small.
