Article
Computer Science, Interdisciplinary Applications
Cathy W. S. Chen, Chun-Shu Chen, Mo-Hua Hsiung
Summary: The study proposes a new model to investigate the spread of infectious diseases. By considering the neighboring locations of the target series, the model presents a continuous conceptualization of distance and highlights the non-separability of space and time. The proposed model successfully captures the characteristics of spatial dependency, over-dispersion, and a large portion of zeros, providing a comprehensive model for the observed phenomena in the data.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Chemistry, Multidisciplinary
Luis Garcia-Gutierrez, Cyril Voyant, Gilles Notton, Javier Almorox
Summary: This study introduces an innovative clustering method for solar radiation stations, utilizing both static and dynamic parameters for easier solar resource forecasting. The research found that only using mean and two dynamic parameters is sufficient to characterize solar irradiation behavior at each site, and recommends using k-means or hierarchical clustering for solar radiation clustering.
APPLIED SCIENCES-BASEL
(2022)
Article
Mathematics, Applied
Inga Kottlarz, Ulrich Parlitz
Summary: The ordinal pattern-based complexity-entropy plane is a popular tool for distinguishing stochastic signals from deterministic chaos. However, its performance has been mainly demonstrated for low-dimensional systems. To evaluate its usefulness for high-dimensional chaotic data, we applied this method to various types of time series. We found that both high-dimensional deterministic data and stochastic surrogate data can occupy the same region on the complexity-entropy plane, making classification challenging. Surrogate data tests based on entropy and complexity provide more significant results.
Article
Geography, Physical
Jiangang Guo, Jinfeng Wang, Chengdong Xu, Yongze Song
Summary: This article reviews the methods for spatial stratified heterogeneity (SSH) and categorizes them into four classes. Given that different stratification methods reflect distinct human understandings of spatial distributions and associations, further studies integrating SSH, advanced algorithms, and interdisciplinary methods are needed to reveal the nature of geographical attributes.
GISCIENCE & REMOTE SENSING
(2022)
Article
Multidisciplinary Sciences
Zsigmond Benko, Tamas Babel, Zoltan Somogyvari
Summary: Recognizing anomalous events is a challenging but critical task in various fields. This paper introduces a new concept called unicorn or unique event and presents a model-free, unsupervised detection algorithm to identify unicorns. The algorithm utilizes Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous datasets from dynamic systems.
SCIENTIFIC REPORTS
(2022)
Article
Meteorology & Atmospheric Sciences
Isa Ebtehaj, Hossein Bonakdari, Pijush Samui, Bahram Gharabaghi
Summary: This study introduces a novel emotional neural network (ENN) for soil temperature modeling. Two different scenarios were considered for forecasting soil temperature, and the proposed ENN model outperformed other modeling techniques in terms of accuracy.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Computer Science, Information Systems
Giuseppe Pernagallo
Summary: This paper introduces an information-based measure of association between time series, called information-based correlation coefficient (ICC), which potentially overcomes some problems related to traditional correlation coefficients. The ICC has advantages such as a more natural interpretation in terms of the reciprocal informativeness between two series, higher reliability in certain processes and conditions, and a well-developed mathematical theory based on entropy. It can also detect nonlinear relationships and handle ordinal data. The paper includes discussions on Monte Carlo simulations, applications to real data, properties of the estimator, and asymptotics under independence.
INFORMATION SCIENCES
(2023)
Article
Physics, Fluids & Plasmas
Tobias Braun, Cinthya N. Fernandez, Deniz Eroglu, Adam Hartland, Sebastian F. M. Breitenbach, Norbert Marwan
Summary: The analysis of irregularly sampled time series is a challenging task. We demonstrate that the edit distance is an effective metric for comparing time series segments of unequal length. We study the impact of sampling rate variations on recurrence quantification analysis and propose a method to correct for biases. The effectiveness of the proposed approach is demonstrated with an example and a real-world dataset.
Article
Computer Science, Information Systems
Julian Cendrero, Julio Gonzalo, Marcos Galletero, Ivar Zapata
Summary: This paper introduces a method called Time Series Impact Through Topic Modeling (TSITM) that models the impact of underlying themes discussed in text data on time series. The method combines latent Dirichlet allocation (LDA) with linear regression, using an elastic net prior to set the impact of uncorrelated topics to zero. Experimental results show that TSITM outperforms baseline and state of the art methods in terms of mean squared error (MSE), mean absolute error (MAE), and out-of-sample R-2.
Article
Environmental Sciences
Taesam Lee, Taha B. M. J. Ouarda
Summary: Hydrological time series often show nonstationarities due to human activities and global climate change. It is important for water managers to identify and define these nonstationarities in hydrological records and appropriately model and simulate them. In this study, three approaches were suggested to address stochastically nonstationary behaviors, and different models were employed to represent these options for hydrological variables. The results indicate that the EMD-NSOR model can reproduce long-term dependence and generate manageable scenarios, while the SML model does not properly reproduce long-term dependence critical for simulating sustainable flood events. It is concluded that nonstationarities in hydrological series should be carefully handled in stochastic simulation models to manage future water-related risks.
Article
Environmental Sciences
Huimin Wang, Songbai Song, Gengxi Zhang, Olusola O. Ayantobo, Tianli Guo
Summary: This study assesses the applicability of SV models to streamflow modeling in the Yellow River basin, and finds that SV models can better describe streamflow series with time-varying variance and accurately capture the occurrence of peak streamflow.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Theory & Methods
Muhammad Aslam
Summary: In this paper, a semi-average method based on neutrosophic statistics is introduced to measure the trend in imprecise or interval data. This method can be applied to imprecise or interval data, which cannot be achieved by the traditional semi-average method in classical statistics. The application of the proposed method is demonstrated using wind speed data, and its efficiency is compared with the classical semi-average method in terms of information and adequacy.
JOURNAL OF BIG DATA
(2023)
Article
Economics
Zhaoxing Gao, Ruey S. Tsay
Summary: This paper introduces a new procedure for building factor models for high-dimensional unit-root time series, which involves a non-singular linear transformation, estimation of stationary common factors, and idiosyncratic white noise components. The proposed method is shown to have good performance in terms of forecasting ability for a 508-dimensional PM2.5 series in Taiwan, compared to other commonly used methods.
INTERNATIONAL JOURNAL OF FORECASTING
(2021)
Review
Chemistry, Analytical
Sio-Iong Ao, Haytham Fayek
Summary: This paper provides a systematic review of recent Deep Learning applications in sensor time series, highlighting the need for advanced preprocessing techniques in certain sensor environments and summarizing how to deploy Deep Learning in time series modeling while mitigating catastrophic forgetting with continual learning methods.
Article
Computer Science, Information Systems
Jinuk Park, Chanhee Park, Jonghwan Choi, Sanghyun Park
Summary: In this study, we propose DeepGate, a time series forecasting framework based on explicit global-local decomposition. By separating the decomposition and prediction modules, DeepGate is able to generate interpretable global series while improving forecasting performance. Experimental results demonstrate that DeepGate outperforms baseline models in time series forecasting tasks.
INFORMATION SCIENCES
(2022)
Article
Fisheries
Pouria Ramazi, Samuel M. Fischer, Julie Alexander, Clayton T. James, Andrew J. Paul, Russell Greiner, Mark A. Lewis
Summary: A graphical model for the establishment and spread of whirling disease has been developed by synthesizing experts' opinions and empirical studies, providing an empirically driven framework for constructing future models.
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
(2022)
Editorial Material
Biology
Philip K. Maini, Mark A. J. Chaplain, Mark A. Lewis, Jonathan A. Sherratt
BULLETIN OF MATHEMATICAL BIOLOGY
(2022)
Article
Biology
Chunxi Feng, Mark A. Lewis, Chuncheng Wang, Hao Wang
Summary: In this paper, a novel free boundary problem is proposed to model the movement of single species with a range boundary. The movement of the species within the range boundary is governed by a reaction-diffusion equation, while the movement of the range limit is influenced by the total population inside the range boundary and is described by an integro-differential equation. The new model is well-posed and possesses steady state, and the spreading speed of the range boundary is slower compared to the equivalent problem with a Stefan condition. The model extends the dichotomy of spreading-vanishing behavior to a trichotomy of spreading-balancing-vanishing.
BULLETIN OF MATHEMATICAL BIOLOGY
(2022)
Article
Ecology
Peter R. Thompson, Andrew E. Derocher, Mark A. Edwards, Mark A. Lewis
Summary: Spatial memory in animals plays a crucial role in determining their movement patterns, which can be observed by ecologists. A model has been developed to understand how animals utilize memory in their environments, allowing for an increased understanding of animal cognition. The model shows potential in identifying specific mechanisms through which animals use memory to optimize their foraging strategies.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Article
Biology
Xiunan Wang, Hao Wang, Pouria Ramazi, Kyeongah Nah, Mark Lewis
Summary: This study combines a mechanistic model with a machine learning algorithm to predict the number of daily confirmed cases of COVID-19 by estimating the transmission rate from non-pharmaceutical policy data. The resulting model accurately forecasts the number of cases in the future and identifies the most informative predictive variables. This research is important for improving prediction models and informing policymakers.
BULLETIN OF MATHEMATICAL BIOLOGY
(2022)
Article
Biology
F. M. Hamelin, Y. Mammeri, Y. Aigu, S. E. Strelkov, M. A. Lewis
Summary: This research explores the spread of disease in host mixtures composed of two genotypes (susceptible and resistant), showing that the disease spread may be split into two fronts led by wild-type and resistance-breaking pathogens. The study demonstrates that host diversification methods can have both positive and negative effects on disease spread compared to a resistant pure stand.
BULLETIN OF MATHEMATICAL BIOLOGY
(2022)
Article
Ecology
Christopher M. M. Heggerud, Hao Wang, Mark A. A. Lewis
Summary: This article proposes a model that combines socio-economic and ecological dynamics to study the impact of human activities on freshwater lakes. The model takes into account the choices made by human populations to mitigate pollution, and reveals the existence of two stable states corresponding to different levels of mitigation efforts and cyanobacteria abundance. The study also examines the social interactions in a network of lakes and demonstrates the influence of social ostracism and pressure on regime shifts between cooperation levels and cyanobacteria abundance.
ECOLOGICAL ECONOMICS
(2022)
Article
Ecology
Natasha J. Klappstein, Jonathan R. Potts, Theo Michelot, Luca Borger, Nicholas W. Pilfold, Mark A. Lewis, Andrew E. Derocher
Summary: The energy selection function (ESF) is introduced as a novel parameterization of step selection functions (SSFs) to evaluate how animals choose habitat based on energetic considerations. The ESF framework combines the energetic consequences of movement and resource selection, providing a key mechanism for habitat selection analysis.
JOURNAL OF ANIMAL ECOLOGY
(2022)
Article
Ecology
Jonathan R. Potts, Valeria Giunta, Mark A. Lewis
Summary: This research investigates the impacts of inter-population interactions on the spatio-temporal distributions of ecosystems, through stochastic individual-based modeling and mathematical analysis, categorizing emergent patterns and demonstrating how environmental features and between-population interactions can lead to different spatial distribution predictions.
Article
Mathematics, Applied
Peter D. Harrington, Mark A. Lewis, P. van den Driessche
Summary: This paper analyzes the transient dynamics in birth-jump metapopulations and discusses the choice of appropriate norms and the effect of stage structure on transient dynamics. It compares different norms and explores the case where transient dynamics are very different than asymptotic dynamics. It also connects the concepts of reactivity and attenuation to the source-sink classification of habitat patches commonly found in marine metapopulations.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2022)
Article
Ecology
Peter R. Thompson, Mark A. Lewis, Mark A. Edwards, Andrew E. Derocher
Summary: Animal movement modeling on a population of brown bears in the Canadian Arctic revealed that a significant proportion of bears utilize complex, time-dependent spatial memory to inform their movement decisions. This highlights the importance of spatial memory as a survival strategy for brown bears in extreme environments.
Article
Biology
Xiunan Wang, Hao Wang, Pouria Ramazi, Kyeongah Nah, Mark Lewis
Summary: Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for public health decision-making. This study extends a method combining mechanistic modeling and machine learning to forecast daily confirmed cases in the US and identify the relative influence of policies. Results show that including non-pharmaceutical intervention data improves prediction accuracy, with restrictions on gatherings, testing, and school closing as the most influential predictor variables.
BULLETIN OF MATHEMATICAL BIOLOGY
(2022)
Article
Biology
Jingjing Xu, Evelyn H. Merrill, Mark A. Lewis
Summary: This study presents a spatio-temporal, differential equation model for understanding the spreading of Chronic Wasting Disease (CWD) in cervid species. The model incorporates important factors such as home range overlap and male dispersal, and assesses the impact of various factors on the spreading speed through sensitivity analysis. The study also evaluates the effect of landscape heterogeneity on the spreading of CWD.
JOURNAL OF THEORETICAL BIOLOGY
(2022)
Article
Biology
Valeria Giunta, Thomas Hillen, Mark A. Lewis, Jonathan R. Potts
Summary: This study provides a method for determining the qualitative structure of local minimum energy states in multi-species nonlocal advection-diffusion models and reveals the rich multi-stability in models of biological processes.
JOURNAL OF MATHEMATICAL BIOLOGY
(2022)
Article
Biodiversity Conservation
Samuel M. Fischer, Pouria Ramazi, Sean Simmons, Mark S. Poesch, Mark A. Lewis
Summary: Managing invasive species and pathogens requires accurate information about potential vectors' traffic. Mobile app data provides new opportunities to improve estimates and analyze vector preferences' impact on propagule flows. However, data reported voluntarily via apps may lack trip records, posing uncertainty. We show how to overcome this drawback and use app-based data to build a stochastic model for angler traffic, improving accuracy and addressing the problem of missing trip records.
JOURNAL OF APPLIED ECOLOGY
(2023)
