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15 results for “univariate”
Data from: Evaluating Window Size Effects on Univariate Time Series Forecasting with Machine Learning
<p>In the realm of time series prediction modeling, the window size (w) is a critical hyperparameter that determines the number of time units included in each example provided to a learning model. This hyperparameter is crucial because it allows the learning model to recognize both long-term and short-term trends, as well as seasonal patterns, while reducing sensitivity to random noise. This study aims to elucidate the impact of window size on the performance of machine learning algorithms in univariate time series forecasting tasks. To achieve this, we employed 40 time series from two different domains, conducting experiments with varying window sizes using four types of machine learning algorithms: Bagging, Boosting, Stacking, and a Recurrent Neural Network (RNN) architecture. The results reveal that increasing the window size generally enhances the evaluation metric values up to a stabilization point, beyond which further increases do not significantly improve predictive accuracy. This stabilization effect was observed in both domains when w values exceeded 100 time steps. Moreover, the study found that RNN architectures do not consistently outperform ensemble models in various univariate time series forecasting scenarios.</p>
DATASET FOR UNIVARIATE TESTS OF OCR SYSTEMS
<p>In this work we present the creation of a repository containing 52,525 synthetic images, generated with features commonly found on digital platforms, and which contains disinformation presented in Portuguese for conducting univariate tests with optical character recognition (OCR) systems. Miscellaneous characteristics in images are: text rotation angle, image dimensions, font color, font size, font style, background color and shadow effect on text. More information can be found at the link: <a href="https://github.com/MaVILab-UFV/OCR-eval-for-misinformation-SIBGRAPI-2023">MaVILab-UFV/OCR-eval-for-misinformation-SIBGRAPI-2023 (github.com)</a></p>
Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
<p>Aim The development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.<br> Location Mediterranean Sea<br> Taxon Dusky spinefoot, Siganus luridus.<br> Methods We analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter-range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models' predictive ability, using independent data from the most recent phase of invasion.<br> Results We detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter-ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift toward lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.<br> Main conclusions We provide the first evidence that using conserved predictors in SDMs improves inter-range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.</p>
Supplemental material for "Statins, type 2 diabetes and body mass index: a univariable and multivariable Mendelian randomization study"
<p>Supplemental material for "Statins, type 2 diabetes and body mass index: a univariable and multivariable Mendelian randomization study" by Guoyi Yang and C Mary Schooling.</p>
Fitting and evaluating univariate and multivariate models of within-lineage evolution
<p><span>The nature of phenotypic evolution within lineages is central to many unresolved questions in paleontology and evolutionary biology. Analyses of evolutionary time-series of ancestor-descendant populations in the fossil record are likely to make important contributions to many of these debates. However, the limited number of models that have been applied to these types of data may restrict our ability to interpret phenotypic evolution in the fossil record. </span>Using uni- and multivariate models of trait evolution <span>that make different assumptions regarding</span> <span>the </span>dynamic<span>s of the</span> adaptive landscape, I evaluate contrasting hypotheses to explain evolution of size in the radiolarian <em><span>Eucyrtidium calvertense</span></em> and armor in the stickleback <em>Gaserosteus doryssus</em>. Body size evolution in <span><em>E. calvertense</em> </span><span>is best explained by a model where the lineage evolves as a consequence of a shift in the adaptive landscape that coincides with the initiation of neosympatry with its sister lineage. Multivariate evolution of armor traits in a stickleback lineage (<em>Gasterosteus</em> <em>doryssus</em>) shows evidence of adaptation towards independent optima on the adaptive landscape at the same time as traits change in a correlated fashion. The fitted models are available in an R package evoTS, which builds on the commonly used paleoTS framework.</span></p>
Table S3. Univariate analyses of early BCR in high-risk and very high-risk patients after propensity score matching.
<p>Table S3. Univariate analyses of early BCR in high-risk and very high-risk patients after propensity score matching.</p>
Table S2. Univariate analyses of PSM in high-risk and very high-risk patients after propensity score matching.
<p>Table S2. Univariate analyses of PSM in high-risk and very high-risk patients after propensity score matching.</p>
Table S1. Univariate analyses of early continence in high-risk and very high-risk patients after propensity score matching.
<p>Table S1. Univariate analyses of early continence in high-risk and very high-risk patients after propensity score matching.</p>
Fitting and evaluating univariate and multivariate models of within-lineage evolution
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Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
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Table S1.Results of Univariate Cox Regression Analysis for Factors Associated with All-Cause Mortality in COPD Figure S1. Sensitivity analysis of COPD all-cause mortality across tertiles of DII
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Data from: Patterns of univariate and multivariate plasticity to elevated carbon dioxide in six European populations of Arabidopsis thaliana
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Data from: Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II
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Causal association between non-steroidal anti-inflammatory drugs use and the risk of benign prostatic hyperplasia: A univariable and multivariable Mendelian randomization study
<p>Raw data using Mendelian randomization to analyze the association between non-steroidal anti-inflammatory drugs and benign prostatic hyperplasia.</p>
Univariate Partition Functions for Planetary Boundary Layer Schemes at Gray Zone Resolutions
<p>This dataset contains the data used in the submitted manuscript of Liu et al. 2023. Please refer to the manuscript for the detailed description of the dataset.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.