Query: Time series data graph

Candidate papers:

abstract: The objective of this paper is to monitor complex process dynamics manifest in multivariate (multidimensional) time series data using a spectral (algebraic) graph theoretic approach. We test the hypothesis that the spectral graph based topological invariants detect incipient process drifts earlier [lower average run length (ARL<sub>1</sub> and with higher fidelity (consistency of detection) when compared with the conventional statistics based approaches. The presented approach maps a multidimensional sensor data stream <inline formula> <tex math notation="LaTeX"\mathcal {X}{N\times d} /tex math>/inline formula> (visualize <inline formula> <tex math notation="LaTeX"$N$ /tex math>/inline formula> as time and <inline formula> <tex math notation="LaTeX"$d$ /tex math>/inline formula> as the number of sensors) as an unweighted and undirected network graph <inline formula> <tex math notation="LaTeX"$G(V,E) /tex math>/inline formula> indexed by its vertices <inline formula> <tex math notation="LaTeX"$V$ /tex math>/inline formula> and edges <inline formula> <tex math notation="LaTeX"$E$ /tex math>/inline formula> i.e. <inline formula> <tex math notation="LaTeX"\mathcal {X}\mapsto G(V,E) /tex math>/inline formula> The rationale is that the graph based topological invariants are surrogate representatives of the system state. We compare the monitoring performance of spectral graph theoretic invariants with conventional statistical features in an exponentially weighted moving average control chart setting. The practical utility of the approach is substantiated in the context of process monitoring in two advanced manufacturing scenarios, namely, ultraprecision machining (UPM) and semiconductor chemical mechanical planarization. These studies corroborate the hypothesis that graph theoretic invariants, when used as monitoring statistics, lead to lower ARL<sub>1</sub> and more consistent detections in contrast to conventional statistical features. For instance, in the UPM case, the fault detection delay using graph theoretic invariants is less than 160 ms, compared with over 8 s of delay with statistical features./p><p>Note to Practitioners This paper addresses the critical problem of capturing process drifts from multidimensional (multisensor) data. The novelty of this paper is the development of a graph theoretic approach that combines signals from multiple in situ sensors for detecting abnormal process drifts. We show that this approach, which invokes graph based topological invariants instead of statistical feature mining, is capable of capturing process drifts at an earlier stage (in terms of detection delay or lower average run length) and higher consistency of detection than conventional statistical features. As a practical consequence of this research, the operator can track the status of a complex process in a tractable control chart setting with only two graph theoretic topological invariants, as opposed to complex black box models involving several features.
author_names: Mohammad Samie Tootooni

Prahalad K Rao

Chun-An Chou

Zhenyu James Kong
title: A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data From Complex Dynamical Processes
venue: IEEE Transactions on Automation Science and Engineering
year: 2018

abstract: The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the cha.
author_names: Pritpal Singh

Gaurav Dhiman

Amandeep Kaur
title: A quantum approach for time series data based on graph and Schrodinger equations methods
year: 2018

abstract: In this paper, we propose a real time algorithm to realize a diagnosis of switched systems for abrupt parametric faults. This algorithm is based on interaction between a Qualitative Diagnosis (QD) and a monitoring component that performs a Qualitative Trend Analysis (QTA) of residual signals generated from Bond Graph (BG) elements called residual sinks. The QTA is applied in order to on line detect change in the mean of residual signal based on combination of Piecewise Aggregate Approximation (PAA) with Page Hinkley Test (PHT) The QD procedure is performed in two stages. In the first off line stage, Symbolic Fault Signature Matrix (SFSM) is generated from a Parameterized Temporal Causal Graph (PTCG) The PTCG is valid for all system modes and deduced from a unified Hybrid Bond Graph (HBG) model by converting its elements into node and labeled edge. Each entry in the SFSM matrix gives the residual symbolic signature which corresponds to the lower order signature predicted using the PTCG model by propagating initial deviation from the fault parameter in the label of edge to the residual node. In the second on line stage, trend extraction by linear regression is triggered after change detection in order to estimate the lower order time derivative symbol for each residual sinks. Subsequently, we propose a stepwise similarity measure for fault isolation task. The functioning of this approach is illustrated in simulating with a switched quarter car active suspension system. Hybrid BG extends the ability of BG to model switched systems using intuitive representation for structural discontinuities.HBG is used as a unified tool for a combination of Quantitative Qualitative FDI appraoch that numerically evaluate residual signalsQualitative Trend Analysis and Page Hinkley Test are used for a real time symbolic signature estimation of residual signalsCausal Graph deduced from HBG and parameterized by the state of the controlled junction is used for symbolic prediction.
author_names: Mariam Taktak

Slim Triki

Anas Kamoun
title: Real time algorithm based on time series data abstraction and hybrid bond graph model for diagnosis of switched system
venue: Eng. Appl. Artif. Intell.
year: 2017

abstract: Abstract Fault diagnosis of photovoltaic array plays an important role in operation and maintenance of PV power plant. The nonlinear characteristics of photovoltaic array and the Maximum Power Point Tracking technology in the inverter prevent conventional protection devices to trip under certain faults which reduces the system's efficiency and increases the risks of fire hazards. In order to better diagnose photovoltaic array faults under Maximum Power Point Tracking conditions, the sequential data of transient in time domain under faults are analyzed and then applied as the input fault features in this work. Firstly, the sequential current and voltage of the photovoltaic array are transformed into a 2 Dimension electrical time series graph to visually represent the characteristics of sequential data. Secondly, a Convolutional Neural Network structure comprising nine convolutional layers, nine max pooling layers, and a fully connected layer is proposed for the photovoltaic array fault diagnosis. The proposed model for photovoltaic array fault diagnosis integrates two main parts, namely the feature extraction and the classification. Thirdly, this model automatically extracts suitable features representation from raw electrical time series graph, which eliminates the need of using artificially established features of data and then employs for photovoltaic fault diagnosis. Moreover, the proposed Convolutional Neural Network based photovoltaic array fault diagnosis method only takes the array of voltage and current of the photovoltaic array as the input features and the reference panels used for normalization. The proposed approach of photovoltaic array fault diagnosis achieved over 99% average accuracy when applied to the case studies. The comparisons of the experimental results demonstrate that the proposed method is both effective and reliable.
author_names: Xiaoyang Lu

Peijie Lin

Shuying Cheng

Yaohai Lin

Zhicong Chen

Lijun Wu

Qianying Zheng
title: Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph
venue: Energy Conversion and Management
year: 2019

abstract: The spatio temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring the spatial properties in local or global scope from spatio temporal graphs simultaneously. To tackle this problem, we design a novel multi scale architecture, Spatio Temporal U Net (ST UNet) for graph structured time series modeling. In this U shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST Unpool) restores the original structure of spatio temporal graphs and resumes regular intervals within graph sequences. Experiments on spatio temporal prediction tasks demonstrate that our model effectively captures comprehensive features in multiple scales and achieves substantial improvements over mainstream methods on several real world datasets.
author_names: Ting Yu

Haoteng Yin

Zhanxing Zhu
title: ST UNet: A Spatio Temporal U Network for Graph structured Time Series Modeling
venue: ArXiv
year: 2019

abstract: Pre processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre processing pipelines, human in the loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in depth research in visual analytics. We present a visual interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty aware pre processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre processing in general and for uncertainty aware pre processing of multivariate time series in particular.
author_names: Jurgen Bernard

Marco Hutter

Heiko Reinemuth

Hendrik Pfeifer

Christian Bors

Jorn Kohlhammer
title: Visual Interactive Preprocessing of Multivariate Time Series Data
venue: Comput. Graph. Forum
year: 2019

abstract: Abstract Shield tunneling operation affects project scheduling, cost estimation, and safety risk during metro construction. Given the importance of this process, tunnel engineers should understand the complexities of shield tunneling parameters. This study addresses the characteristics of shield tunneling parameters by introducing a visibility graph model implemented in a complex network of shield tunneling in metro construction. Time series data are collected for the analysis of six parameters generated in each segment ring tunneling cycle. These parameters are total thrust, torque, penetration rate, rotation rate, advance speed, and working chamber pressure. Bridging time series and the complex network with visibility graph algorithm indicates that all degree distributions of the construct networks follow the power law, establishing the scale free time series of the parameters. Small world features and hierarchical structures also indicate that the fluctuation and variance of the parameters are attracted, limited, or affected by a previous shield operation of segment rings. In addition, findings reveal turning points in the parameters' time series with vital node identification based on complex network analysis. Implications relevant for shield operators and managers are proposed to improve shield tunneling performance, efficiency, and safety.
author_names: Cheng Zhou

Lieyun Ding

Ying Zhou

Miros aw J Skibniewski
title: Visibility graph analysis on time series of shield tunneling parameters based on complex network theory
venue: Tunnelling and Underground Space Technology
year: 2019

abstract: The unsupervised detection of anomalies in time series data has important applications, e.g. in user behavioural modelling, fraud detection, and cybersecurity. Anomaly detection has been extensively studied in categorical sequences, however we often have access to time series data that contain paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies we must account for the fact that such data contain a large number of independent observations of short paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem we introduce a novel framework for the unsupervised detection of anomalies in large corpora of variable length temporal paths in a graph, which provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.
author_names: Timothy LaRock

Vahan Nanumyan

Ingo Scholtes

Giona Casiraghi

Tina Eliassi-Rad

Frank Schweitzer
title: Detecting Path Anomalies in Time Series Data on Networks
venue: ArXiv
year: 2019

abstract: Abstract With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real time. Detecting events from Twitter stream requires complex techniques. Event related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph based approach, called the Enhanced Heartbeat Graph (EHG) which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real world datasets (i.e. Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state of the art techniques.
author_names: Zafar Saeed

Rabeeh Ayaz Abbasi

Imran Razzak

Onaiza Maqbool

Abida Sadaf

Guandong Xu
title: Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks
venue: Expert Syst. Appl.
year: 2019

abstract: Narrative and storytelling have played an important role in communication. In this work, we demonstrate that the techniques of storytelling, and in particular raising attention to abnormality, can be used to add interestingness and memorability to descriptions of time series data. A computational system has been developed for automatically generating descriptions for data graphs. The system identifies visual patterns in the graph, and treats the graph's deviations from corresponding ideal patterns as abnormal events. It then uses storytelling templates to generate a graph description with the abnormal events highlighted.
author_names: Zev Battad

Mei Si
title: Apply Storytelling Techniques for Describing Time Series Data
venue: ICIDS
year: 2018