Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration

Timeboxes are rectangular widgets that can be used in direct-manipulation graphical user interfaces (GUIs) to specify query constraints on time series data sets. Timeboxes are used to specify simultaneously two sets of constraints: given a set of N time series profiles, a timebox covering time periods x1… x2 ( x1 ≤ x2) and values y1… y2 ( y1 ≤ y2) will retrieve only those n√N that have values y1 ≤ y2 during all times x1 ≤ x ≤ x2. TimeSearcher is an information visualization tool that combines timebox queries with overview displays, query-by-example facilities, and support for queries over multiple time-varying attributes. Query manipulation tools including pattern inversion and ‘leaders & laggards’ graphical bookmarks provide additional support for interactive exploration of data sets. Extensions to the basic timebox model that provide additional expressivity include variable time timeboxes, which can be used to express queries with variability in the time interval, and angular queries, which search for ranges of differentials, rather than absolute values. Analysis of the algorithmic requirements for providing dynamic query performance for timebox queries showed that a sequential search outperformed searches based on geometric indices. Design studies helped identify the strengths and weaknesses of the query tools. Extended case studies involving the analysis of two different types of data from molecular biology experiments provided valuable feedback and validated the utility of both the timebox model and the TimeSearcher tool. Timesearcher is available at http://www.cs.umd.edu/hcil/timesearcher

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Time series event correlation with DTW and Hierarchical Clustering methods

10.7287/peerj.preprints.27959 ◽

2019 ◽

Author(s):

Srishti Mishra ◽

Zohair Shafi ◽

Santanu Pathak

Keyword(s):

Time Series ◽

Hierarchical Clustering ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Multiple Time ◽

Clustering Methods ◽

Event Correlation ◽

Data Set ◽

Causation Analysis

Data driven decision making is becoming increasingly an important aspect for successful business execution. More and more organizations are moving towards taking informed decisions based on the data that they are generating. Most of this data are in temporal format - time series data. Effective analysis across time series data sets, in an efficient and quick manner is a challenge. The most interesting and valuable part of such analysis is to generate insights on correlation and causation across multiple time series data sets. This paper looks at methods that can be used to analyze such data sets and gain useful insights from it, primarily in the form of correlation and causation analysis. This paper focuses on two methods for doing so, Two Sample Test with Dynamic Time Warping and Hierarchical Clustering and looks at how the results returned from both can be used to gain a better understanding of the data. Moreover, the methods used are meant to work with any data set, regardless of the subject domain and idiosyncrasies of the data set, primarily, a data agnostic approach.

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Time series event correlation with DTW and Hierarchical Clustering methods

10.7287/peerj.preprints.27959v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Srishti Mishra ◽

Zohair Shafi ◽

Santanu Pathak

Keyword(s):

Time Series ◽

Hierarchical Clustering ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Multiple Time ◽

Clustering Methods ◽

Event Correlation ◽

Data Set ◽

Causation Analysis

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Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽

10.3390/ai2010005 ◽

2021 ◽

Vol 2 (1) ◽

pp. 48-70

Author(s):

Wei Ming Tan ◽

T. Hui Teo

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Remaining Useful Life ◽

Sensor Data ◽

Series Data ◽

Multiple Time ◽

Data Set ◽

Form Complex ◽

Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽

10.3390/w13141944 ◽

2021 ◽

Vol 13 (14) ◽

pp. 1944

Author(s):

Haitham H. Mahmoud ◽

Wenyan Wu ◽

Yonghao Wang

Keyword(s):

Time Series ◽

Distribution Systems ◽

Water Distribution ◽

Time Series Data ◽

Series Data ◽

Process Time ◽

Time Interval ◽

Security Level ◽

Data Validation ◽

Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

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Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

10.1101/170027 ◽

2017 ◽

Author(s):

Anthony Szedlak ◽

Spencer Sims ◽

Nicholas Smith ◽

Giovanni Paternostro ◽

Carlo Piermarocchi

Keyword(s):

Neural Network ◽

Gene Expression ◽

Cell Cycle ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Expression Data ◽

Time Series Gene Expression ◽

Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

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Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data

e-Journal of Analysis and Applied Mathematics ◽

10.2478/ejaam-2020-0008 ◽

2020 ◽

Vol 2020 (1) ◽

pp. 98-117

Author(s):

Jyoti U. Devkota

Keyword(s):

Time Series ◽

Infrared Imaging ◽

Time Series Data ◽

Spline Interpolation ◽

Radiant Heat ◽

Series Data ◽

Time Interval ◽

Industrial Sites ◽

Steel Mills ◽

Gas Flares

Abstract The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.

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Representing Unevenly-Spaced Time Series Data for Visualization and Interactive Exploration

Human-Computer Interaction - INTERACT 2005 - Lecture Notes in Computer Science ◽

10.1007/11555261_66 ◽

2005 ◽

pp. 835-846 ◽

Cited By ~ 18

Author(s):

Aleks Aris ◽

Ben Shneiderman ◽

Catherine Plaisant ◽

Galit Shmueli ◽

Wolfgang Jank

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Interactive Exploration

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An Efficient Method for Forecasting Using Fuzzy Time Series

Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-0914-1.ch013 ◽

2017 ◽

pp. 287-304 ◽

Cited By ~ 3

Author(s):

Pritpal Singh

Keyword(s):

Time Series ◽

Time Series Data ◽

Weather Forecasting ◽

Small Error ◽

Fuzzy Time Series ◽

Series Data ◽

Data Sets ◽

Proposed Model ◽

Temperature Forecasting ◽

The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

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