Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

AbstractMany studies have revealed that both host and environmental factors can impact the gut microbial compositions, implying that the gut microbiota is considerably dynamic1–5. In their Article, Ji et al.6 performed comprehensive analysis of multiple high-resolution time series data of human and mouse gut microbiota. They found that both human and mouse gut microbiota dynamics can be characterized by several robust scaling laws describing short- and long-term changes in gut microbiota abundances, distributions of species residence and return times, and the correlation between the mean and the temporal variance of species abundances. They claimed that those scaling laws characterize both short- and long-term dynamics of gut microbiota. However, we are concerned that their interpretation is quite misleading, because all the scaling laws can be reproduced by the shuffled time series with completely randomized time stamps of the microbiome samples.

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Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

10.1101/2020.08.16.20175901 ◽

2020 ◽

Cited By ~ 1

Author(s):

Anushka Bhaskar ◽

Jay Chandra ◽

Danielle Braun ◽

Jacqueline Cellini ◽

Francesca Dominici

Keyword(s):

Air Pollution ◽

Time Series ◽

Health Outcomes ◽

Study Design ◽

Air Pollutant ◽

Short Term ◽

Cross Sectional ◽

Short And Long Term ◽

Long Term Studies

Background: As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. Objectives: To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. Method: We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results: 27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. Discussion: We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

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Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2013.2273574 ◽

2014 ◽

Vol 25 (1) ◽

pp. 241-246 ◽

Cited By ~ 5

Author(s):

Domingos Savio Pereira Salazar ◽

Paulo Jorge Leitao Adeodato ◽

Adrian Lucena Arnaud

Keyword(s):

Time Series ◽

Nonstationary Time Series ◽

Short And Long Term

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A First Public Research Collection of High-Resolution Latent Fingerprint Time Series for Short- and Long-Term Print Age Estimation

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2017.2705622 ◽

2017 ◽

Vol 12 (10) ◽

pp. 2276-2291 ◽

Cited By ~ 4

Author(s):

Ronny Merkel ◽

Jana Dittmann ◽

Claus Vielhauer

Keyword(s):

Time Series ◽

High Resolution ◽

Age Estimation ◽

Public Research ◽

Latent Fingerprint ◽

Research Collection ◽

Short And Long Term

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INTRODUCTION TO TIME-VARYING MODELING WITH MACROECONOMIC AND FINANCIAL DATA

Macroeconomic Dynamics ◽

10.1017/s136510051100071x ◽

2012 ◽

Vol 16 (S2) ◽

pp. 167-175 ◽

Cited By ~ 6

Author(s):

Fredj Jawadi

Keyword(s):

Time Series ◽

Time Variation ◽

Moving Average ◽

Statistical Properties ◽

Financial Data ◽

Time Varying ◽

Arch Model ◽

Financial Variables ◽

Short And Long Term

The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econometric protocols to take such changes into consideration. The introduction of ARMA (autoregressive moving average models) by Box and Jenkins (1970) led to the development of time-series econometrics, which had a major impact on the conceptual analysis of economic and financial data. This type of modeling offered a transition from a static setup to a new modeling process that reproduces the time-varying features of macroeconomic and financial series. However, the ARMA modeling system retains the constancy of the first and second moments, limits the phases of a cycle to symmetrical instances, and only reproduces the dynamics of stationary variables. It thus fails to adequately reproduce the nonstationary relationships between major economic and financial variables. Abrupt changes in economies and financial systems have given evidence of nonstationary series whose statistical properties are also time-varying, making it necessary to develop new econometric tools to capture the time variation of economic and financial series in the mean and in the variance, and to apprehend their dynamics in the short and long term. Among the most important and influential studies in the 1980s' econometrics literature were therefore those that dealt with the introduction of the ARCH (autoregressive conditional heteroskedasticity) model by Engle (1982) and the cointegration theory by Engle and Granger (1987). The ARCH model, which focuses on the time-varying features of volatility structure, was a major breakthrough, as it highlighted the importance of the second moment of time series, while the cointegration framework enabled the short- and long-term dynamics of nonstationary variables to be modeled.

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Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights

Mathematics ◽

10.3390/math9212817 ◽

2021 ◽

Vol 9 (21) ◽

pp. 2817

Author(s):

Pushpa Dissanayake ◽

Teresa Flock ◽

Johanna Meier ◽

Philipp Sibbertsen

Keyword(s):

Time Series ◽

Long Range ◽

High Threshold ◽

Peaks Over Threshold ◽

Significant Wave ◽

Wave Heights ◽

Short And Long Term ◽

Significant Wave Heights ◽

Threshold Exceedances

The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short- and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short- and long-range dependence to address extremal clustering, and their theoretical justification.

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Artificial mimicry of seasonal transcriptome dynamics in Arabidopsis thaliana reveals short- and long-term responses to environmental conditions

10.1101/2021.08.02.454700 ◽

2021 ◽

Author(s):

Yuko Kurita ◽

Hironori Takimoto ◽

Mari Kamitani ◽

Yoichi Hashida ◽

Makoto Kashima ◽

...

Keyword(s):

Time Series ◽

Arabidopsis Thaliana ◽

Wild Population ◽

Low Cost ◽

Smart Growth ◽

Experimental System ◽

Arabidopsis Halleri ◽

Short Term ◽

Short And Long Term

Plants must respond to various environmental factors that change seasonally. In a previous study, seasonally oscillating genes were identified by a massive time-series transcriptome analysis in a wild population of Arabidopsis halleri ssp. gemmifera, a sister species of Arabidopsis thaliana. To analyze the function of these seasonally oscillating genes, we established an experimental system to mimic seasonal expression trends using A. thaliana. Arabidopsis thaliana plants were cultured under conditions that mimicked average monthly temperatures and daylengths in a "smart growth chamber mini," a hand-made low-cost small chamber. Under different short-term incubations, the seasonal trends of 1627 seasonally oscillating genes were mimicked. These seasonally oscillating genes had varying temporal responsiveness (constant, transient, and incremental). Our findings suggest that plants perceive and integrate information about environmental stimuli in the field by combining seasonally oscillating genes with temporal responsiveness.

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