Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

2019 ◽  
Author(s):  
Zachary Zimmerman ◽  
Nader Shakibay Senobari ◽  
Gareth Funning ◽  
Evangelos Papalexakis ◽  
Samet Oymak ◽  
...  
Keyword(s):  
Time Series ◽  
Fast Moving ◽  
The Face ◽  
Time Series Mining

Honor Among Thieves: Understanding Rhetorical and Material Cooperation Among Violent Nonstate Actors

2021 ◽  
pp. 1-40
Author(s):  
Christopher W. Blair ◽  
Erica Chenoweth ◽  
Michael C. Horowitz ◽  
Evan Perkoski ◽  
Philip B.K. Potter
Keyword(s):  
Time Series ◽  
Strategic Behavior ◽  
Time Series Data ◽  
Transnational Networks ◽  
Series Data ◽  
Nonstate Actors ◽  
State Repression ◽  
Security Risks ◽  
Community Monitoring ◽  
The Face

Abstract Cooperation among militant organizations contributes to capability but also presents security risks. This is particularly the case when organizations face substantial repression from the state. As a consequence, for cooperation to emerge and persist when it is most valuable, militant groups must have means of committing to cooperation even when the incentives to defect are high. We posit that shared ideology plays this role by providing community monitoring, authority structures, trust, and transnational networks. We test this theory using new, expansive, time-series data on relationships between militant organizations from 1950 to 2016, which we introduce here. We find that when groups share an ideology, and especially a religion, they are more likely to sustain material cooperation in the face of state repression. These findings contextualize and expand upon research demonstrating that connections between violent nonstate actors strongly shape their tactical and strategic behavior.

An approach based on satellite image time series mining to identify region susceptible to desertification

2014 ◽  
Author(s):  
R. Scrivani ◽  
R. R. V. Goncalves ◽  
L. A. S. Romani ◽  
S. R. M. Oliveira ◽  
E. D. Assad
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Time Series Mining

scholarly journals Time-series genome-centric analysis unveils bacterial response to operational disturbance in activated sludge

2019 ◽  
Author(s):  
María Victoria Pérez ◽  
Leandro D. Guerrero ◽  
Esteban Orellana ◽  
Eva L. Figuerola ◽  
Leonardo Erijman
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Microbial Communities ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Operating Conditions ◽  
The Face

ABSTRACTUnderstanding ecosystem response to disturbances and identifying the most critical traits for the maintenance of ecosystem functioning are important goals for microbial community ecology. In this study, we used 16S rRNA amplicon sequencing and metagenomics to investigate the assembly of bacterial populations in a full-scale municipal activated sludge wastewater treatment plant over a period of three years, including a period of nine month of disturbance, characterized by short-term plant shutdowns. Following the reconstruction of 173 metagenome-assembled genomes, we assessed the functional potential, the number of rRNA gene operons and thein situgrowth rate of microorganisms present throughout the time series. Operational disturbances caused a significant decrease in bacteria with a single copy of the ribosomal RNA (rrn) operon. Despite only moderate differences in resource availability, replication rates were distributed uniformly throughout time, with no differences between disturbed and stable periods. We suggest that the length of the growth lag phase, rather than the growth rate, as the primary driver of selection under disturbed conditions. Thus, the system could maintain its function in the face of disturbance by recruiting bacteria with the capacity to rapidly resume growth under unsteady operating conditions.IMPORTANCEIn this work we investigated the response of microbial communities to disturbances in a full-scale activated sludge wastewater treatment plant over a time-scale that included periods of stability and disturbance. We performed a genome-wide analysis, which allowed us the direct estimation of specific cellular traits, including the rRNA operon copy number and the in situ growth rate of bacteria. This work builds upon recent efforts to incorporate growth efficiency for the understanding of the physiological and ecological processes shaping microbial communities in nature. We found evidence that would suggest that activated sludge could maintain its function in the face of disturbance by recruiting bacteria with the capacity to rapidly resume growth under unsteady operating conditions. This paper provides relevant insights into wastewater treatment process, and may also reveal a key role for growth traits in the adaptive response of bacteria to unsteady environmental conditions.

The Experience of Time During William’s Invasion

2020 ◽  
pp. 16-51
Author(s):  
Tony Claydon
Keyword(s):  
Communication Technology ◽  
Fast Moving ◽  
Experience Of Time ◽  
The Face ◽  
Sense Of Time ◽  
William Iii ◽  
Modern Sense

Chapter one examines reactions to the fast-moving events of the autumn of 1688, when James II’s regime collapsed in the face of an invasion by William III. It demonstrates that some features of the reaction illustrate a ‘modern’ sense of time with an unstable present shaping a fluid future (especially the acceleration of time produced by fast-flowing events, and faster flowing news); but it also shows that interruptions in communication technology disrupted this modernity, leading to a fragmented sense of time’s passage, which encouraged the sort of simplistic scripting and produced the sort of bewilderment that may be characteristic of the postmodern condition.

Time Series Mining

2008 ◽  
pp. 14-43
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Dimension Reduction ◽  
Similarity Measures ◽  
Sequential Patterns ◽  
Sequence Mining ◽  
Periodic Patterns ◽  
Background Material ◽  
Time Series Mining ◽  
Data Discretization

In this chapter, we will first give the background and review existing works in time series mining. The background material will include commonly used similarity measures and techniques for dimension reduction and data discretization. Then we will examine techniques to discover periodic and sequential patterns. This will lay the groundwork for the subsequent three chapters on mining dense periodic patterns, incremental sequence mining, and mining progressive patterns.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

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