Temporal variations in benthic assemblages and processes

Demonstrating changes over time in soft-sediment ecosystems is critical to understanding ecosystem dynamics and predicting how they may change. Monitoring is thus an essential process providing insight into how complex ecological systems change and has important implications in adaptive management, impact assessment and stewardship. The chapter describes how both slow and fast processes operate in soft sediments and drive changes across multiple time scales. The role of time series data in helping to understand detailed short-term studies is discussed. The interactions between space and time have important implications in study design, interpretation and accounting for inconsistency in results. The chapter finishes by discussing two types of temporal change of significant concern these days due to their implications for resilience and ecosystem dynamics: tipping points and hysteresis.

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STFT-LDA: An algorithm to facilitate the visual analysis of building seismic responses

Information Visualization ◽

10.1177/14738716211038618 ◽

2021 ◽

pp. 147387162110386

Author(s):

Zhenge Zhao ◽

Danilo Motta ◽

Matthew Berger ◽

Joshua A Levine ◽

Ismail B Kuzucu ◽

...

Keyword(s):

Time Series ◽

Time Scales ◽

Visual Analysis ◽

Time Series Data ◽

Series Data ◽

Multiple Time Scales ◽

Prototype System ◽

Multiple Time ◽

Seismic Responses ◽

Large Ensembles

Civil engineers use numerical simulations of a building’s responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to determine the latter to prevent the former. Such simulations generate large ensembles of multivariate, multiattribute time series. Comprehensive understanding of this data requires techniques that support the multivariate nature of the time series and can compare behaviors that are both periodic and non-periodic across multiple time scales and multiple time series themselves. In this paper, we present a novel technique to extract such patterns from time series generated from simulations of seismic responses. The core of our approach is the use of topic modeling, where topics correspond to interpretable and discriminative features of the earthquakes. We transform the raw time series data into a time series of topics, and use this visual summary to compare temporal patterns in earthquakes, query earthquakes via the topics across arbitrary time scales, and enable details on demand by linking the topic visualization with the original earthquake data. We show, through a surrogate task and an expert study, that this technique allows analysts to more easily identify recurring patterns in such time series. By integrating this technique in a prototype system, we show how it enables novel forms of visual interaction.

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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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Non-bank financial institutions and economic growth: Evidence from Africa’s three largest economies

South African Journal of Economic and Management Sciences ◽

10.4102/sajems.v20i1.1545 ◽

2017 ◽

Vol 20 (1) ◽

Cited By ~ 2

Author(s):

Ronald Rateiwa ◽

Meshach J. Aziakpono

Keyword(s):

Economic Growth ◽

South Africa ◽

Financial Institutions ◽

Time Series Data ◽

Bank Lending ◽

Series Data ◽

The Sustainable Development ◽

Long Run ◽

The Relationship

Background: In order for the post-2015 world development agenda – termed the sustainable development goals (SDGs) – to succeed, there is a pronounced need to ensure that available resources are used more effectively and additional financing is accessed from the private sector. Given that traditional bank lending has slowed down, the development of non-bank financing has become imperative. To this end, this article intends to empirically test the role of non-bank financial institutions (NBFIs) in stimulating economic growth.Aim: The aim of this article is to empirically test the existence of a long-run equilibrium relationship between economic growth and the development of NBFIs, and the causality thereof.Setting: The empirical assessment uses time-series data from Africa’s three largest economies, namely, Egypt, Nigeria and South Africa, over the period 1971–2013.Methods: This article uses the Johansen cointegration and vector error correction model within a country-specific setting.Results: The results showed that the long-run relationship between NBFI development and economic growth is relatively stronger in Egypt and South Africa, than in Nigeria. Evidence in respect of Nigeria shows that such a relationship is weak. The nature of the relationship between NBFI development and economic growth in Egypt is positive and significant, and predominantly bidirectional. This suggests that a virtuous relationship between NBFIs and economic growth exists in Egypt. In South Africa, the relationship is positive and significant and predominantly runs from NBFI development to economic growth, implying a supply-leading phenomenon. In Nigeria, the results are weak and mixed.Conclusion: The study concludes that in countries with more developed financial systems, the role of NBFIs and their importance to the economic growth process are more pronounced. Thus, there is need for developing policies targeted at developing the NBFI sector, given their potential to contribute to economic growth.

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The Role of Technical Innovation and Development of Industrial Sector in Korean International Business

International Journal of Business and Society ◽

10.33736/ijbs.3162.2021 ◽

2021 ◽

Vol 22 (1) ◽

pp. 55-73

Author(s):

Ali Mohammed Khalel Al-Shawaf ◽

Tahira Yasmin

Keyword(s):

Time Series Data ◽

Positive Impact ◽

Generalized Method Of Moments ◽

Value Added ◽

Industrial Sector ◽

Least Square ◽

Policy Implications ◽

Series Data ◽

Significance Level

With the pace of development and competitiveness, innovation plays an important role to capture the market share. Various countries have effective strategies to enhance Research and Development (R&D) and exchange value added products in international market. So, based on this the aim of this research is to examine the role of R&D, industrial design and charges for intellectual property in innovative exports in South Korean economy. Time series data for the period 1998 to 2017, Ordinary Least Square (OLS) and Generalized Method of Moments (GMM) models are used to determine the dynamic interrelationship among the study variables. In summary, the overall results show that there is co-integration rank of in both trace test and value test at 1% significance level. Moreover, OLS and GMM findings depict that there is significant and positive coefficient for ID & RD which represent that they have positive impact on HT. Whereas, the IP displays a negative and significant relationship with high technology exports accordingly. Lastly, the diagnostic tests show that model is stable for the study time period and result is reliable. The current study also suggests some policy implications which can enhance innovative export products of South Korea while enhancing R&D.

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The Performativity of Organizational Culture in a No Excuses Charter School

10.31235/osf.io/8tnfj ◽

2018 ◽

Author(s):

Jason Radford

Keyword(s):

Organizational Culture ◽

Charter School ◽

Multiple Time Scales ◽

Multiple Time ◽

Successful Performance ◽

High Performing ◽

Clear Insight ◽

Cyclical Process ◽

Insight Into ◽

Successes And Failures

Theories developed by academics influence those they study, in some cases fundamentally shaping the world we study. This influential relationship, often called performativity, has gone largely unnoticed and uncommented on in organizational theory and research. The few studies investigating performativity in organizations or other fields typically focus on cases in which the ultimate success of theory's implementation is known. In this paper, I examine how one high-performing charter school sought to turn a prescribed organizational culture into reality. I find that path to successful performance is very narrow and ambiguous. The school succeeded and failed in many steps of the process, making it difficult to assess whether the initiative was successful and to attribute their successes and failures to the theory or their implementation. I conclude that performativity is a cyclical process occurring at multiple time scales. During these cycles, organizations iteratively test new implementations of the theory, seeking to gain clear insight into the success of their strategy and correctly attribute their successes and failures to decide whether the theory actually works or not.

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Trend Recognition in Clinical Signals using Template-Based Methods

Methods of Information in Medicine ◽

10.1055/s-0038-1634287 ◽

2000 ◽

Vol 39 (02) ◽

pp. 101-104

Author(s):

A. Lowe ◽

M. J. Harrison ◽

R. W. Jones

Keyword(s):

Goodness Of Fit ◽

Time Series Data ◽

Series Data ◽

Multiple Time ◽

Linguistic Rules ◽

System Technique ◽

Time Signals ◽

Medical Diagnostic ◽

Clinically Significant ◽

Diagnostic Applications

Abstract:The recognition of clinically significant trends in monitored signals plays an important role in many medical diagnostic applications. A template-based system technique to identify characteristic patterns in time-series data is described, based on fuzzy logic. Fuzzy set theory allows the creation of fuzzy templates from linguistic rules. The resulting fuzzy template system can accommodate multiple time signals, relative or absolute trends, and automatically generates a normalised “goodness of fit” score. The template approach was originally developed for monitoring during anaesthesia but has the potential to be useful in other domains that require temporal pattern recognition.

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