统计研究 ›› 2020, Vol. 37 ›› Issue (5): 94-103.doi: 10.19343/j.cnki.11-1302/c.2020.05.008

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互联网企业广告收入预测研究——基于低频数据的神经网络和时间序列组合模型

吴翌琳 南金伶   

  • 出版日期:2020-05-25 发布日期:2020-05-12

Forecasting of Advertisement Income of Internet Companies—Based on Neural Network and Time Series Model for Low-Frequency Data

Wu Yilin & Nan Jinling   

  • Online:2020-05-25 Published:2020-05-12

摘要: 神经网络模型对大样本时间序列的拟合效果优于传统时间序列模型,但对于年度、月度、日度等低频时间序列的预测则难以发挥其优势。鉴于此,本文应用传统时间序列模型和神经网络模型,建立Holtwinters-BP组合模型,利用Holtwinters模型分别拟合各解释变量序列,利用BP模型拟合解释变量和自变量的非线性关系,基于某社交新闻类APP的日广告收入数据进行互联网企业广告收入预测研究。通过与循环神经网络(RNN)模型、长短期记忆神经网络(LSTM)模型等预测结果的对比发现:Holtwinters-BP组合模型的预测精度和稳定性更高;证明多维变量对于广告收入的显著影响,多变量模型的预测准确性高于单变量模型;构建的Holtwinters-BP组合模型对于低频数据预测有较好的有效性和适用性。

关键词: 广告收入预测, 神经网络模型, Holtwinters模型, 组合模型

Abstract: The fitting effect of neural network model on large sample time series is often better than that of traditional time series models, but it may not be advantageous for the prediction of low-frequency time series such as annual, monthly and daily data. In view of this, we propose HoltwintersBP combined model to forecast the advertisement income and expand the multi-dimensional explanatory variables, using daily advertisement income data from a social news type app. By comparing the prediction results with those of RNN and LSTM, it is found that Holtwinters-BP combined model has higher prediction accuracy and stability; explanatory variables have a significant impact on advertisement income, the prediction accuracy of the model with explanatory variables is higher than that of single variable model; it also verifies the validity and applicability of Holtwinters-BP combined model for low-frequency data prediction.

Key words: Advertisement Income Forecasting, Neural Network Model, Holtwinters Model, Combined Model