Hierarchical Multiresolution Representation of Streaming Time Series

Abstract

Abstract Real-time monitoring, analysis and operations in large industrial systems require an accurate but compact data model created on the basis of a large number of data sources continuously generating massive amounts of data modeled as streaming time series. This paper proposes a generic time series representation approach for reducing data model size and supporting streaming time series data mining at multiple time resolutions. The proposed Hierarchical Multiresolution Time Series Representation model utilizes a buffer-based approach that combines one-pass stream processing with hierarchical aggregation to achieve high processing speed without excessive hardware requirements. In addition, this paper presents a new representation based on the proposed model, Hierarchical Multiresolution Linear-function-based Piecewise Statistical Approximation. The proposed representation considers fluctuations and continuity of modeled processes in order to preserve fundamental characteristics of time series at reduced dimensionality. The usefulness of the proposed solution was proven in a case study. The case study results for generated data set confirm that the proposed model leads to higher and more stable processing speed at lower RAM consumption comparing to related model, especially when dealing with greater number of time resolutions. The case study results for real UK smart meter data confirm that the proposed representation leads to a reduced amount of information loss and an improvement in subsequent time series clustering comparing to related time series representation. Therefore, this paper s main contribution is multiresolution streaming time series data mining support convenient for application in large industrial systems.