基于特征再抽象(FRA)的多元时序预测方法

Multivariate Time Series Forecasting Method Based on FRA

科技领域的衍生行业因普遍存在强时间约束的特性而累积了海量的高维时间序列数据,严峻的数据压力导致传统的数据建模预测方法受制于数据规模和属性维度。支撑高质量的服务对大数据智能预测技术提出了更高的要求,如何在数据层面上实现预测性能的提升是现阶段亟待解决的主要问题。针对上述问题,提出了针对多元时序数据的特征再抽象(Feature Re-Abstraction,FRA)算法,首先通过RobustSTL分解算法提取趋势性和季节性特征(Trend and Seasonality Features,TSFs),实现多元数据的特征二阶抽象,以“抽象即特征”替代传统“标签即特征”的提取策略,再通过Pearson相关系数的运算结果评估再抽象技术捕捉的TSFs与目标参数间的相关强度,证实TSF的数据价值。在FRA算法的基础上结合深度学习模型构建基于数据驱动的多元时序预测算法,通过预测效果验证FRA算法的有效性。实验结果表明,引入TSFs作为数据驱动模型的训练向量能够兼具数据降维、降噪及强相关特性地维持,从而避免模型过拟合并缓解模型欠拟合,提高时序预测算法的准确性和鲁棒性。

Derivative industries in the field of science and technology have accumulated a large amount of high-dimensional time series data due to the general existence of strong time constraints.Severe data pressure makes traditional data modeling and prediction methods limited by data scale and attribute dimensions.Services supporting high-quality put forward higher requirements for big data intelligent prediction technology.How to improve the prediction performance at the data level is a main problem that needs to be solved urgently at this stage.Combined with the above problems,a feature re-abstraction(FRA)algorithm for multivariate time series data is proposed.First,the RobustSTL decomposition algorithm is used to extract trend and seasonality features(TSFs),realize the second-order abstraction of features of multivariate data,and replace the traditional extraction strategy of“labels are features”with“abstract is features”.Then,the correlation strength between the TSFs captured by the re-abstract technology and the target parameters is evaluated by the calculation result of the Pearson correlation coefficient,which confirms the data value of the TSF.On the basis of FRA algorithm,combined with deep learning model,a data-driven multivariate time series prediction algorithm is constructed,and the effectiveness of FRA algorithm is verified by the prediction effect.Experimental results show that the introduction of TSFs as the training vector of the data-driven model can maintain the characteristics of data dimensionality reduction,noise reduction and strong correlation,so as to avoid model overfitting and alleviate model underfitting,and improve the accuracy and robustness of time series prediction algorithms.