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Algebraic Evolutionary Forecasting of Short Time Series

机译:短时间序列的代数演化预测

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A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is proposed in this paper. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series and to extrapolate the model of the process into future. Evolutionary algorithms are used to remove the noise, to identify the skeleton algebraic sequence and to balance the forecast with the smoothed moving average estimate of the time series. Numerical experiments with a real-world time series are used to illustrate the functionality of the proposed technique.
机译:提出了一种基于骨架代数序列识别的短期时间序列预测新方法。 Hankel矩阵的秩的概念被用于检测时间序列的基本片段,并将过程模型外推到未来。进化算法用于消除噪声,识别骨架代数序列,并使预测与时间序列的平滑移动平均值估计保持平衡。具有实际时间序列的数值实验用于说明所提出技术的功能。

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