| 用近邻算法预测通信量时间序列 |
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| 引用本文： | 黄健聪,万海,郝小卫,李磊.用近邻算法预测通信量时间序列[J].计算机科学,2005,32(7):31-33. |
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| 作者姓名： | 黄健聪 万海 郝小卫 李磊 |
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| 作者单位： | 1. 中山大学软件研究所,广州,510275
2. 中山大学信息科学与技术学院,广州,510275 |
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| 摘 要： | 为了对通信系统进行有效的调控，需要对通信量进行预测，而通信量具有在不同日期遵循不同规律的特点。本文采用基于实例的近邻算法进行时间序列预测，并在考虑动态长度序列、序列特征提取和近似样例的选取上做出改进，取得很好的效果。将近邻预测算法应用到广东省电话网智能管理系统(GTNIMS)中，能够为路由求解提供快速、准确的预测话务量，为更精确的求解创造了条件。
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| 关 键 词： | 通信量 时间序列预测 智能管理系统 通信系统 基于实例 特征提取 预测算法 度序列 电话网 广东省 话务量 求解 路由 |
Apply Nearest Neighbor Algorithm to Traffic Time Series Prediction |
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| HUANG Jian-Cong,WAN Hai,HAO Xiao-Wei,Li Lei.Apply Nearest Neighbor Algorithm to Traffic Time Series Prediction[J].Computer Science,2005,32(7):31-33. |
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| Authors: | HUANG Jian-Cong WAN Hai HAO Xiao-Wei Li Lei |
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| Affiliation: | HUANG Jian--Cong,WAN Hai,HAO Xiao-Wei,Li Lei Software Research Institute of SUN YAT-SEN University,Guangzhou 510275 Department of Computer Science and Technology,SUN YAT-SEN University,Guangzhou 510275 |
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| Abstract: | In order to coofigure communication system efficiently, it is necessary to predict the traffic, which has the characteristic of following various rules in different date. This paper proposes time series prediction by Case-based nearest neighbor algorithm and make improvements on considering dynamic length of time series, extracting charac- teristic from time series and selecting nearest case, with which ideal results can be achieved. Being applied to Guang- dong Telecommunications Network Intelligent Management System (GTNIMS), the nearest neighbor algorithm can provide fast, accurate prediction traffic for routing solution and set the stage for more precise solution. |
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| Keywords: | Time series Prediction Nearest neighbor algorithm Traffic |
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