scholarly journals How are species interactions structured in species-rich communities? A new method for analysing time-series data

2017 ◽  
Vol 284 (1855) ◽  
pp. 20170768 ◽  
Author(s):  
Otso Ovaskainen ◽  
Gleb Tikhonov ◽  
David Dunson ◽  
Vidar Grøtan ◽  
Steinar Engen ◽  
...  
Keyword(s):  
Time Series ◽  
Species Interactions ◽  
Time Series Data ◽  
Interspecific Interactions ◽  
Real Data ◽  
Community Level ◽  
Series Data ◽  
Individual Species ◽  
Validation Data ◽  
Species Pairs

Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

scholarly journals Time series forecasting of styrene price using a hybrid ARIMA and neural network model

2019 ◽  
Vol 10 (3) ◽  
pp. 915
Author(s):  
Ali Ebrahimi Ghahnavieh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Real Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Data Sets ◽  
Combined Model ◽  
Future Behavior ◽  
Hybrid Forecasting ◽  
Ann Models

Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

scholarly journals Enhanced accuracy of rainfall–runoff modeling with wavelet transform

2012 ◽  
Vol 15 (2) ◽  
pp. 392-404 ◽  
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Rainfall Time Series ◽  
Calibration Data ◽  
Validation Data ◽  
Original Scale ◽  
Using Data

Wavelet transform (WT) is typically used to decompose time series data for only one hydrological feature at a time. This study applied WT for simultaneous decomposition of rainfall and runoff time series data. For the calibration data, the decomposed rainfall and runoff time series calibrate the subsystem response function using the least squares (LS) method at each scale. For the validation data, the decomposed rainfall time series are convoluted with the estimated subsystem response function to obtain the estimated runoff at each scale. The estimated runoff at the original scale can be obtained by wavelet reconstruction. The efficacy of the proposed method is evaluated in two case studies of the Feng-Hua Bridge and Wu-Tu watershed. The analytic results confirm that the proposed wavelet-based method slightly outperforms the conventional method of using data only at the original scale. The results also show that the runoff hydrograph estimated by using the proposed method is smoother than that obtained using a single scale.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

scholarly journals Quantifying community responses to environmental variation from replicate time series

2021 ◽  
Author(s):  
Joseph S. Phillips ◽  
Lucas A. Nell ◽  
Jamieson C. Botsch
Keyword(s):  
Time Series ◽  
Community Composition ◽  
Mixed Models ◽  
Time Series Data ◽  
Environmental Variation ◽  
Community Change ◽  
Community Level ◽  
Series Data ◽  
Observation Error ◽  
Temporal Autocorrelation

AbstractTime-series data for ecological communities are increasingly available from long-term studies designed to track species responses to environmental change. However, classical multivariate methods for analyzing community composition have limited applicability for time series, as they do not account for temporal autocorrelation in community-member abundances. Furthermore, traditional approaches often obscure the connections between responses at the community level and those for individual taxa, limiting their capacity to infer mechanisms of community change. We show how linear mixed models that account for group-specific temporal autocorrelation and observation error can be used to infer both taxon- and community-level responses to environmental predictors from replicated time-series data. Variation in taxon-specific responses to predictors is modeled using random effects, which can be used to characterize variation in community composition. Moreover, the degree of autocorrelation is estimated separately for each taxon, since this is likely to vary due to differences in their underlying population dynamics. We illustrate the utility of the approach by analyzing the response of a predatory arthropod community to spatiotemporal variation in allochthonous resources in a subarctic landscape. Our results show how mixed models with temporal autocorrelation provide a unified approach to characterizing taxon- and community-level responses to environmental variation through time.

scholarly journals Time Series Clustering Based on the K-Means Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Oleg Kobylin ◽  
Vyacheslav Lyashenko
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
High Similarity ◽  
Time Series Clustering ◽  
Data Presentation

Time series is one of the forms of data presentation that is used in many studies. It is convenient, easy and informative. Clustering is one of the tasks of data processing. Thus, the most relevant currently are methods for clustering time series. Clustering time series data aims to create clusters with high similarity within a cluster and low similarity between clusters. This work is devoted to clustering time series. Various methods of time series clustering are considered. Examples are given for real data.

Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks

2014 ◽  
Vol 926-930 ◽  
pp. 1886-1889
Author(s):  
Bo Tian ◽  
Dian Hong Wang ◽  
Fen Xiong Chen ◽  
Zheng Pu Zhang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Series Data ◽  
Feature Space ◽  
Real Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Edge Operator ◽  
Abnormal Events

This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.

scholarly journals Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations

Genetics ◽  
2020 ◽  
Vol 216 (2) ◽  
pp. 521-541
Author(s):  
Zhangyi He ◽  
Xiaoyang Dai ◽  
Mark Beaumont ◽  
Feng Yu
Keyword(s):  
Time Series ◽  
Natural Selection ◽  
Time Series Data ◽  
Genetic Recombination ◽  
Real Data ◽  
Series Data ◽  
Accurate Estimation ◽  
Inference Procedure ◽  
Posterior Probability Distribution ◽  
Recent Action

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.

scholarly journals Estimadores Suavizados de Cópulas via Ondaletas

2010 ◽  
Vol 8 (3) ◽  
pp. 263 ◽  
Author(s):  
Pedro Alberto Morettin ◽  
Clélia Maria De Castro Toloi ◽  
Chang Chiann ◽  
José Carlos Simon De Miranda
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Real Data ◽  
Series Data

We introduce copula estimators based on wavelet smoothing of empirical copulas for the case of time series data. We then study the properties of this estimator via simulations and compare its performance with other estimators. Applications to real data are also given.

scholarly journals New statistical model for misreported data with application to current public health challenges

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Moriña ◽  
Amanda Fernández-Fontelo ◽  
Alejandra Cabaña ◽  
Pedro Puig
Keyword(s):  
Public Health ◽  
Time Series ◽  
Human Papillomavirus ◽  
Continuous Time ◽  
Time Series Data ◽  
Real Data ◽  
Current Data ◽  
Series Data ◽  
Proposed Model ◽  
Public Health Challenges

AbstractThe main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

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