基于ε-SVR的用户视听在线人数预测

预测视听在线人数能够帮助厂商提供有价值的信息, 获取更大效益. 从时间序列分析出发, 经过特征调整, 利用支持向量回归对用户视听在线人数进行准确预测. 首先, 对数据进行时间序列分析建模并预测; 然后, 将模型视为线性回归对用户视听在线人数作进一步改进, 结合时间与实际生活中的特征进行调整, 并添加了新的特征; 接着, 对新特征组成的样本进行支持向量回归, 通过社会认知优化寻找径向基函数中的最优参数; 最后, 得到比较理想的预测效果.

Predicting number of online users byε-SVR

Predicting the number of online audio-visual users can provide valuable information to help manufacturers get more profits. Based on time series analysis, support vector regression is used to make accurate prediction with adjusted feature. The time series is first modeled and predicted, a linear regression model used to make further improvement, and then, by combining time and real-life characteristics, adding a new feature. Samples of the new feature are trained with support vector regression. Optimal parameters of the radial basis function are sought using the social cognitive optimization. A good prediction result can be obtained using the proposed method.