还提出了滑动窗口的更新周期、数据流的流速对预测精度影响的数学模型。
A mathematical model that characterizes the affects of the updating cycle of sliding window and data stream rate on predictive accuracy is also presented.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
为了提高数据流中异常数据的预测速度与精度，提出一种基于稀疏表示的数据流异常数据预测方法。
This paper proposed a new prediction method for outliers over data stream based on sparse representation to improve the optimum prediction speed and performance of outliers over data stream.
研究数据流上的历史数据的变化趋势，并预测数据流在未来时间窗口内的可能值是数据流挖掘的一项重要工作。
It is also an important work to study the varying tendency of the historic stream data ina data stream and predict the possible values of the stream data in the future time window.
研究数据流上的历史数据的变化趋势，并预测数据流在未来时间窗口内的可能值是数据流挖掘的一项重要工作。
It is also an important work to study the varying tendency of the historic stream data ina data stream and predict the possible values of the stream data in the future time window.
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