Manifold regularization based semi-supervised regression on multivariate time series
Xiaobin Li · Journal of China University of Mining and Technology · 2011
Traditional semi-supervised regression on multivariate time series only takes account of the spatial information of samples,and the temporal information is always neglected.To solve the problem,a semi-supervised regression algorithm ST-LapRLSR is proposed which takes account of the temporal information of samples.For time series an assumption of temporal smooth is proposed,and based on this assumption,a regularization item that could reflect more underline information of samples is constructed.During constructing graph Laplacian,the temporal relation of samples is used in computing edge weight.Semi-supervised regression under manifold regularization framework using the proposed regularization item is carried out,and solved by the Representer theorem.The experiments take on public dataset and multivariate sensor time series data of mine,and the results show that,in semi-supervised regression on multivariate time series,ST-LapRLSR which uses temporal and spatial information of samples simultaneously achieves better accuracy contrast to LapRLSR which only considers the spatial information of samples.