scholarly journals NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

2021 ◽  
Author(s):  
Samantha J Gleich ◽  
Jacob A Cram ◽  
Jake L Weissman ◽  
David A Caron
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Species Abundance ◽  
Covariance Structure ◽  
Additive Models ◽  
Series Data ◽  
Ecological Network ◽  
Network Analyses ◽  
Abundance Data ◽  
Species Abundance Data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. Additionally, the GAM transformation increased the F1 score of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

scholarly journals Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1343 ◽  
Author(s):  
Robin A. Choudhury ◽  
Neil McRoberts
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Complex Dynamics ◽  
Cost Effective ◽  
Series Data ◽  
Spore Trap ◽  
Abundance Data ◽  
Airborne Inoculum ◽  
Pathogen Dna ◽  
Low Dimensional

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

scholarly journals Reconstructing ecological networks with noisy dynamics

2020 ◽  
Vol 476 (2237) ◽  
pp. 20190739 ◽  
Author(s):  
Mara A. Freilich ◽  
Rolando Rebolledo ◽  
Derek Corcoran ◽  
Pablo A. Marquet
Keyword(s):  
Time Series ◽  
Network Topology ◽  
Network Structure ◽  
Time Series Data ◽  
Species Abundance ◽  
Intrinsic Noise ◽  
Ecological Networks ◽  
Series Data ◽  
Statistical Techniques ◽  
Time Series Data Analysis

Ecosystems functioning is based on an intricate web of interactions among living entities. Most of these interactions are difficult to observe, especially when the diversity of interacting entities is large and they are of small size and abundance. To sidestep this limitation, it has become common to infer the network structure of ecosystems from time series of species abundance, but it is not clear how well can networks be reconstructed, especially in the presence of stochasticity that propagates through ecological networks. We evaluate the effects of intrinsic noise and network topology on the performance of different methods of inferring network structure from time-series data. Analysis of seven different four-species motifs using a stochastic model demonstrates that star-shaped motifs are differentially detected by these methods while rings are differentially constructed. The ability to reconstruct the network is unaffected by the magnitude of stochasticity in the population dynamics. Instead, interaction between the stochastic and deterministic parts of the system determines the path that the whole system takes to equilibrium and shapes the species covariance. We highlight the effects of long transients on the path to equilibrium and suggest a path forward for developing more ecologically sound statistical techniques.

scholarly journals Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems

2016 ◽  
Author(s):  
Alex Washburne ◽  
Daniel Lacker ◽  
Josh Burby
Keyword(s):  
Time Series ◽  
Population Genetics ◽  
Time Series Data ◽  
Covariance Structure ◽  
Individual Performance ◽  
Series Data ◽  
Static System ◽  
Neutrality Test ◽  
Tests Of Neutrality ◽  
System Properties

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system's dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.

scholarly journals Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 216 ◽  
Author(s):  
Dongmei Ai ◽  
Xiaoxin Li ◽  
Gang Liu ◽  
Xiaoyi Liang ◽  
Li Xia
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Granger Causality ◽  
Large Scale ◽  
Community Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Association Network ◽  
Causal Relationships ◽  
Network Analyses

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

scholarly journals FORECASTING SEASONAL TIME SERIES DATA USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD OF ADDITIVE MODELS

2020 ◽  
Vol 16 (2) ◽  
pp. 151
Author(s):  
Nurhamidah Nurhamidah ◽  
Nusyirwan Nusyirwan ◽  
Ahmad Faisol
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Additive Model ◽  
Exponential Smoothing ◽  
Additive Models ◽  
Smoothing Method ◽  
Series Data ◽  
Seasonal Time Series ◽  
Exponential Smoothing Method ◽  
Forecasting Errors

The purpose of this study was to predict seasonal time series data using the Holt-Winters exponential smoothing additive model.  The data used in this study is data on the number of passengers departing at Hasanudin Airport in 2009-2019, the source of the data obtained from the official website of the Central Statistics Agency.  The results showed that the Holt-Winters exponential smoothing method on the passenger's number at Hasanudin Airport in 2009 to 2019 contained trend patterns and seasonal patterns, by first determining the initial values and smoothing parameters that could minimize forecasting errors.

scholarly journals Characterization of Pathogen Air-Borne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

2020 ◽  
Author(s):  
Robin A Choudhury ◽  
Neil McRoberts
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stochastic Dynamics ◽  
Complex Dynamics ◽  
Series Data ◽  
Spore Trap ◽  
Abundance Data ◽  
Pathogen Dna ◽  
Low Dimensional ◽  
Species Specific

Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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