scholarly journals Long-term seasonality of marine photoheterotrophic bacteria reveals low cohesiveness within the different phylogroups

2018 ◽  
Author(s):  
Adrià Auladell ◽  
Pablo Sánchez ◽  
Olga Sánchez ◽  
Josep M. Gasol ◽  
Isabel Ferrera
Keyword(s):  
Time Series ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Niche Partitioning ◽  
Operational Taxonomic Unit ◽  
Amplicon Sequencing ◽  
Day Length ◽  
M Gene ◽  
Nw Mediterranean

AbstractAerobic anoxygenic phototrophic (AAP) bacteria play a relevant role in the marine microbial food web, but little is known about their long-term seasonal dynamics. Using Illumina amplicon sequencing of thepufM gene coupled with multivariate, time series and co-occurrence analyses we examined their temporal dynamics over a decade at the Blanes Bay Microbial Observatory (NW Mediterranean). Phylogroup K (Gammaproteobacteria) was the most abundant over all seasons, with phylogroups E and G (Alphaproteobacteria) being often abundant in spring. A clear seasonal trend was observed in diversity, with maximum values in winter. Multivariate analyses showed sample clustering by season, with a relevant proportion of the variance (38%) explained by day length, temperature, salinity, phototrophic nanoflagellate abundance and phosphate concentration. Time series analysis showed that only 42% of the Amplicon Sequence Variants (ASVs) analyzed presented marked seasonality but these represented most of the abundance (92%). Interestingly, distinct temporal dynamics were observed within the same phylogroup and even within different ASVs conforming the same Operational Taxonomic Unit (OTU). Likewise, co-occurrence analysis highlighted negative associations between various ASVs within the same phylogroup. Altogether our results picture the AAP assemblage as highly seasonal, containing ecotypes with distinctive niche partitioning rather than being a cohesive functional group.

scholarly journals Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

2020 ◽  
Vol 34 (04) ◽  
pp. 5956-5963
Author(s):  
Xianfeng Tang ◽  
Huaxiu Yao ◽  
Yiwei Sun ◽  
Charu Aggarwal ◽  
Prasenjit Mitra ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Missing Values ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Temporal Distribution ◽  
Joint Modeling ◽  
Adversarial Training ◽  
Global Patterns ◽  
Real World Datasets

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.

scholarly journals A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence

2021 ◽  
Author(s):  
Hieu M. Nguyen ◽  
Philip Turk ◽  
Andrew McWilliams
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Healthcare Systems ◽  
Leading Indicators ◽  
Short Term ◽  
Correction Model ◽  
Vector Error Correction ◽  
Infection Incidence ◽  
Vector Error

AbstractCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Fore-casting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. In the literature, only a few papers have approached this problem from a multivariate time-series approach incorporating leading indicators for the hospital census. In this paper, we propose to use a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework using a Vector Error Correction model (VECM) and aim to forecast the COVID-19 hospital census for the next 7 days. The model is also applied to produce scenario-based 60-day forecasts based on different trajectories of the pandemic. With several hypothesis tests and model diagnostics, we confirm that the two time-series have a cointegration relationship, which serves as an important predictor. Other diagnostics demonstrate the goodness-of-fit of the model. Using time-series cross-validation, we can estimate the out-of-sample Mean Absolute Percentage Error (MAPE). The model has a median MAPE of 5.9%, which is lower than the 6.6% median MAPE from a univariate Autoregressive Integrated Moving Average model. In the application of scenario-based long-term forecasting, future census exhibits concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully in-corporated into a VECM with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.Author summaryDuring the COVID-19 pandemic, healthcare systems need to have adequate resources to accommodate demand from COVID-19 cases. One of the most important metrics for planning is the COVID-19 hospital census. Only a few papers make use of leading indicators within multivariate time-series models for this problem. We incorporated a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework called the Vector Error Correction model to make 7-day-ahead forecasts. This model is also applied to produce 60-day scenario forecasts based on different trajectories of the pandemic. We find that the two time-series have a stable long-run relationship. The model has a good fit to the data and good forecast performance in comparison with a more traditional model using the census data alone. When applied to different 60-day scenarios of the pandemic, the census forecasts show concave trajectories that peak 2-3 weeks later than the infection incidence. Our paper presents this new model for accurate short-term forecasts and realistic scenario-based long-term forecasts of the COVID-19 hospital census to help healthcare systems in their decision making. Our findings suggest using the local COVID-19 infection incidence data can improve and extend more traditional forecasting models.

scholarly journals Spatial and temporal dynamics of SAR11 marine bacteria sampled across a nearshore to offshore transect in the tropical Pacific Ocean

2021 ◽  
Author(s):  
Sarah J. Tucker ◽  
Kelle C. Freel ◽  
Elizabeth A. Monaghan ◽  
Clarisse E.S. Sullivan ◽  
Oscar Ramfelt ◽  
...  
Keyword(s):  
Time Series ◽  
Ribosomal Rna ◽  
Marine Bacteria ◽  
Temporal Dynamics ◽  
Amplicon Sequencing ◽  
Ecological Niches ◽  
Tropical Pacific Ocean ◽  
Time Period ◽  
Spatial And Temporal Dynamics ◽  
The Tropical Pacific

AbstractTime-series surveys of microbial communities coupled with contextual measures of the environment provide a useful approach to dissect the factors determining distributions of microorganisms across ecological niches. Here, monthly time-series samples of surface seawater along a transect spanning the nearshore coastal environment within Kāne‘ohe Bay on the island of O‘ahu, Hawai‘i, and the adjacent offshore environment were collected to investigate the diversity and abundance of SAR11 marine bacteria over a two-year time period. Using 16S ribosomal RNA gene amplicon sequencing, the spatiotemporal distributions of major SAR11 subclades and individual amplicon sequence variants (ASVs) were evaluated. On average, 77% of the SAR11 community was compromised of a small number of ASVs (7 of 106 in total), which were ubiquitously distributed across all samples collected from one or both of the end-member environments sampled in this study (coastal or offshore). SAR11 ASVs were more often restricted spatially to coastal or offshore environments (64 of 106 ASVs) than they were shared among coastal, transition, and offshore environments (39 of 106 ASVs). Overall, offshore SAR11 communities contained a higher diversity of SAR11 ASVs than their nearshore counterparts. This study reveals ecological differentiation of SAR11 marine bacteria across a short physiochemical gradient, further increasing our understanding of how SAR11 genetic diversity partitions into distinct ecological units.

Nonstationary Stochastic Modelling of Multivariate Long-Term Wind and Wave Data

2005 ◽  
Author(s):  
Christos N. Stefanakos ◽  
Konstandinos A. Belibassakis
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Multivariate Time Series ◽  
Stochastic Modelling ◽  
Mean Value ◽  
Wave Data ◽  
Available Information ◽  
Periodic Components ◽  
Box Cox Transformation

In the present work, a nonstationary stochastic model, which is suitable for the analysis and simulation of multivariate time series of wind and wave data, is being presented and validated. This model belongs to the class of periodically correlated stochastic processes with yearly periodic mean value and standard deviation (periodically correlated or cyclostationary stochastic process). First, the time series is appropriately transformed to become Gaussian using the Box-Cox transformation. Then, the series is decomposed, using an appropriate seasonal standardization procedure, to a periodic (deterministic) mean value and a (stochastic) residual time series multiplied by a periodic (deterministic) standard deviation. The periodic components are estimated using appropriate time series of monthly data. The residual stochastic part, which is proved to be stationary, is modelled as a VARMA process. This way the initial process can be given the structure of a multivariate periodically correlated process. The present methodology permits a reliable reproduction of available information about wind and wave conditions, which is required for a number of applications.

Intra-annual fluctuations dominating temporal dynamics of benthic diatom assemblages in a Chinese mountainous river

2020 ◽  
Vol 56 ◽  
pp. 22
Author(s):  
Shuhan Guo ◽  
Fengzhi He ◽  
Tao Tang ◽  
Lu Tan ◽  
Qinghua Cai
Keyword(s):  
Community Dynamics ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Multiple Scale ◽  
Benthic Diatom ◽  
Diatom Community ◽  
Diatom Communities ◽  
Mountainous River

Understanding temporal dynamics of community may provide insights on biological responses under environmental changes. However, our knowledge on temporal dynamics of river organisms is still limited. In the present study, we employed a multivariate time-series modeling approach with a long-term dataset (i.e. 72 consecutive months) to investigate temporal dynamics of benthic diatom communities in four sites located in a Chinese mountainous river network. We hypothesized that: (1) there are multi-scale temporal dynamics within the diatom community; (2) intra-annual fluctuations dominate the community dynamics; (3) diatom species composing the community respond distinctly to environmental changes. We found that intra-annual fluctuations with periodicities <12 months explained 8.1–16.1% of community variation. In contrast, fluctuations with periodicities of 13–36 months and 37–72 months only accounted for 1.1–5.9% and 2.8–9.7% of variance in diatom community dynamics, respectively. Taxa correlating significantly to each significant RDA axis (namely, RDA taxa group) displayed distinct temporal dynamics. Conductivity, total nitrogen, and pH were important to most RDA taxa groups across the four sites while their effects were group-specific. We concluded that intra-annual dynamics dominated temporal variation in diatom communities due to community responses to local environmental fluctuations. We suggest that long-term monitoring data are valuable for identifying multiple-scale temporal dynamics within biological communities.

Exploring long-term urban cycles with multivariate time-series analysis

2021 ◽  
pp. 239980832110581
Author(s):  
Luca Salvati
Keyword(s):  
Time Series ◽  
Life Cycle ◽  
Time Series Analysis ◽  
Population Growth ◽  
Multivariate Time Series ◽  
Metropolitan Region ◽  
Series Analysis ◽  
Multivariate Time Series Analysis ◽  
Non Linear

European cities underwent long-term socioeconomic transformations resulting in a shift from centralized demographic growth typical of late industrialization to a more recent (and spatially uncoordinated) de-concentration of population and economic activities. While abandoning traditional compact models and moving toward settlement dispersion, population growth in urban areas was assumed to follow a “life cycle” constituted of four developmental stages (urbanization, suburbanization, counter-urbanization, and re-urbanization). We studied anomalies in the City Life Cycle (CLC) of a large metropolitan region (Athens, Greece) with the aim at achieving a less mechanistic interpretation of long-term population growth in complex social contexts. Using population data that cover more than 170 years (1848–2020) and multivariate time-series analysis, a non-linear growth history was delineated, with sequential accelerations and decelerations characteristic of the first CLC stage (urbanization). Considering the classical division in three radio-centric districts (core, ring, and agglomeration), different development stages coexisted since World War II. Heterogeneous suburbanization processes mixed up with late urbanization and weaker impulses of counter-urbanization and re-urbanization. The empirical results of time-series analysis confirm the non-linear expansion of Athens, shedding further light on long-term mechanisms of metropolitan development and informing management policies of urban growth.

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