An Adaptive Forecasting Method for Time-Series Data Streams
An Adaptive Forecasting Method for Time-Series Data Streams
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DOI：
10.1360/aas-007-0197
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Wang Yong
中科院分区：
文献类型：
--
作者：
Wang Yong
提出了一种结合人工智能预测方法的精度和时间序列预测方法的快速性优点的自适应预测方法AFStreams。该算法能自适应地根据流值变化率估计预测步长，并能在有限的资源下以最小的计算代价生成经过验证的最优预测点轨迹。实验证明，AFStreams能够很好地适应数据的变化，在计算复杂度和预测精度之间取得了很好的平衡。
An adaptive forecasting method that combines the merits of the precision of artificial intelligence forecasting method and the rapidness of times-series forecasting method, called AFStreams, is proposed. It can estimate the forecasting-step self adaptively from the change ratio of stream-values and can generate proved optimal track of forecasting points with the minimum computation cost from limited resources. Experiments proved that AFStreams can adapt to the changes of data well and provide tradeoff between computing complexity and forecasting precision.