Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure

Sedimentary lithofacies, petrography and diagenesis of the Kapuni group in the Kapuni Field, Taranaki Basin, New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science with Honours in Earth Science at Massey University, Palmerston North, New Zealand

The reservoir architecture and quality of the Kapuni Group sandstones in seven wells (Kapuni−1, −3, −8, −12, Deep−1, 14 and −15) in the Kapuni Field are characterised using available core and digital geophysical log data. The study focused primarily on the Eocene Mangahewa Formation, but where limited core permits the older Kaimiro and Farewell formations are also examined. Eleven lithofacies in the Kapuni Group, identified and defined in core on the basis of colour, lithology, bedding, texture and sedimentary structures, are interpreted to represent tidal sand bar, tidal-inlet channel, fluvial-tidal channel, spit platform, sand flat, shallow marine, tidal channel, meandering tidal channel, mud flat, swamp and marsh environments. Correlation of core lithofacies with geophysical log motifs enabled lithofacies identification where core data are not available. Log motifs representing each of the lithofacies were then extrapolated to uncored sections of the Mangahewa Formation in the Kapuni Field wells. Interpretation of lithofacies in core and geophysical log motifs indicate that the Mangahewa Formation was deposited in an estuarine setting. During initial deposition of the Mangahewa Formation tide-dominated estuarine lithofacies were deposited. A major coal horizon, the K20 coal, in the field represents a period of maximum infilling. Above this coal core and log data indicate a wave-dominated estuary exhibiting a clearly- defined, "tripartite" (coarse-fine-coarse) distribution of lithofacies. Provenance studies suggest that low-grade metamorphic and granitic rocks are the dominant source for the Kapuni Group sandstones. Minor input from sedimentary and acid volcanic source rocks are also identified. A volcanic source, however, is more important in sandstones from the Farewell Formation, than in the younger Kapuni Group formations. Probable sources include the low-grade metamorphic rocks of Lower Cambrian to Permian age, Permian to Carboniferous Karamea Granite, Triassic and Jurassic greywacke-argillite sediments. Upper Cretaceous Pakawau Group sediments and Pre Cambrian to Upper Cretaceous acid volcanics. Reservoir quality variations in the Kapuni Group sandstones are directly related to environmental and diagenetic processes that have controlled porosity reduction and enhancement. Porosity has been reduced mainly by mechanical and chemical compaction, clay formation (predominantly kaolinite and illite in the Mangahewa and Kaimiro formations and smectite in the Farewell Formation), carbonate precipitation (primarily siderite and calcite), quartz and feldspar overgrowths and pyrite precipitation. While, porosity has been enhanced primarily by carbonate dissolution and subordinately by grain and clay dissolution and minor grain fracturing. The Mangahewa Formation sandstone lithofacies of tidal sand bar and tidal channel environments exhibit the best reservoir characteristics. Future reservoir development in the Kapuni Field and exploration in the Kapuni Field should focus on identifying and exploiting these lithofacies

Ranking and significance of variable-length similarity-based time series motifs

The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that such variable-length similarity-based motifs cannot be directly compared, and hence ranked, by their normalized dissimilarities. Specifically, we find that length-normalized motif dissimilarities still have intrinsic dependencies on the motif length, and that lowest dissimilarities are particularly affected by this dependency. Moreover, we find that such dependencies are generally non-linear and change with the considered data set and dissimilarity measure. Based on these findings, we propose a solution to rank those motifs and measure their significance. This solution relies on a compact but accurate model of the dissimilarity space, using a beta distribution with three parameters that depend on the motif length in a non-linear way. We believe the incomparability of variable-length dissimilarities could go beyond the field of time series, and that similar modeling strategies as the one used here could be of help in a more broad context.Comment: 20 pages, 10 figure