Abstract Multivariable time series (MTS) clustering is an important topic in data mining. The major challenge of MTS to capture the temporal correlations and dependencies between multiple variables. In this paper, we propose a novel approach based on graph convolutional network (GCN), which powerful feature extractor for structure data. We regard each variable as node construct edges through co...

Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. We present a novel method to fully reconstruct MODIS daily LST products for ce...

Abstract Multivariate time series are widely used in industrial equipment monitoring and maintenance, health monitoring, weather forecasting other fields. Due to abnormal sensors, failures, environmental interference human errors, the collected multivariate usually have certain missing values. Missing values imply regularity of data, seriously affect further analysis application series. Convent...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.012 ⇑ Corresponding author. Tel.: +36 88 624209. E-mail address: [email protected] (J. Ab In recent years, dynamic time warping (DTW) has begun to become the most widely used technique for comparison of time series data where extensive a priori knowledge is not available. However, it is often expe...

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Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new dista...