一种模糊时间序列概率预测方法

A probabilistic forecasting method with fuzzy time series

在时序预测任务中,历史观测值的不确定性给预测带来了困难。而模糊时间序列预测方法在处理数据不确定性方面具有独特的优势。概率预测则能够提供预测目标的分布情况,从而量化预测结果的不确定性。因此,为了减少不确定性对预测任务的影响,提出了一种基于概率加权策略的模糊时间序列概率预测方法。该方法利用预测目标的历史观测值建立概率加权的模糊时间序列预测模型,通过引入额外的观测值对预测模型的模糊规则库进行细化。在细化过程中,使用2种计算成本较低的算子重构模糊逻辑关系。具体地,交算子用于剔除干扰的信息,并算子则融合所有信息,从而得到2个不同的模糊逻辑关系组集合。当前时刻观测值在2个集合中对应的模糊逻辑关系组即为对下一时刻模糊集的预测,最后经过解模糊输出下一时刻的概率分布。在公开时间序列数据集上验证了该方法的准确性和有效性,与近期提出的PWFTS预测方法相比,预测精度有显著提高。

In time series prediction tasks,the uncertainty of historical observations poses difficulties in forecasting.However,the forecasting methods based on fuzzy time series have unique advantages in dealing with data uncertainty.Probabilistic forecasting,on the other hand,can provide the distribution of the predicted target and quantify the uncertainty of the prediction results.Therefore,a fuzzy time series probabilistic forecasting method based on a probability weighting strategy is proposed to reduce the impact of uncertainty on the forecasting task.The proposed method builds a probability-weighted fuzzy time series prediction model using historical observations of the target variable,and refines the fuzzy rule base of the prediction model by introducing additional observations.Specifically,two operators with low computational cost are used to reconstruct the fuzzy logic relationships.The intersection operator is used to exclude the interfering information,while the union operator merges all information,resulting in two different sets of fuzzy logic relationship groups.The relationship group corresponding to the current observation value in two sets is the prediction for the fuzzy set in the next moment.Finally,the probability distribution of the next moment is output by defuzzification.Experimental results on publicly available time series data sets verify the accuracy and validity of this method,and the prediction accuracy is remarkably improved in comparison to the newly proposed PWFTS prediction method.