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27 results for “Autoregressive models”
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models
<p>This dataset is used in the paper "Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models" by Massimo Guidolin and Giulia F. Panzeri. The data contains daily observations of the VIX, SKEW, and SKEW− indices over the period January 4, 1996, - December 31, 2019. This period covers a total of 6,005 daily observations. The dataset is constructed from multiple sources as described in the paper and includes several files that correspond to different transformations and forecast errors of the indices.</p>
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
1.Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. 2.We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent-offspring dyads identified with 16 microsatellites. 3.We used a novel autoregressive multi-season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model-averaged dispersal distance. 4.As predicted, colonization was more strongly related to connectivity at small spatial scales (< 10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. 5.Model-averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean (± SE) dispersal distance from 23 parent-offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization.
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
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Joint Autoregressive and Graph Models for Software and Developer Social Networks
<p>This zip contains three CSV files and one folder. This dataset contains information for the recent ten distributions.</p> <ul> <li><strong>developer_attributes.csv</strong>: There are seven columns in this file. "distro" (str) represents distribution name. "source" (str) denotes source package name. "person_id" (str) indicates developer identity. "closes" (int), "high" (int), "medium" (int), "low" (int) are the features.</li> <li><strong>source_bugs.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents the source package name. "bug_count" (int) denotes the number of bugs that source package has at a particular distribution.</li> <li><strong>source_sizes.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents source package name. "size" (int) denotes the size of the package.</li> <li><strong>Dependency folder:</strong> Within this folder, ten dependency lists are present. Each file contains two columns i.e "start" (str) and "target" (str). Both of them represent source packages. So, we read as the "start" source package depends on "target" source package. </li> </ul> <p>Here is the arxiv version of our paper: <a href="https://arxiv.org/abs/2101.08729">https://arxiv.org/abs/2101.08729</a>. </p> <p>Here is the portal link: <a href="https://sites.google.com/view/rima-hazra/swnet">https://sites.google.com/view/rima-hazra/swnet</a></p>
Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
1.Time series of animal movement obtained from bio-loggers are becoming widely used across all taxa. These data are nowadays of high quality, combining high resolution with precision, as the tags are able to collect for longer times and store larger quantities of data. Due to their nature, high-frequency data sequences often pose non-trivial problems in time series analysis: non-linearity, non-Normality, non-stationarity, and long memory. These issues can be tackled by modelling the data sequence as a realization of a stochastic regime switching process. 2. We suggest a novel Markov switching autoregressive model where the hidden Markov chain is non-homogeneous, with time-varying transition probabilities, whose dynamics depend on the dynamics of some contemporary categorical covariates. 3. To illustrate the use of the method, we apply it to the depth profiles of four individuals of flapper skate (Dipturus cf. intermedia) in order to identify swimming behaviours. Individual time series were obtained from data storage tags that recorded pressure every two minutes. The environmental covariates used were lunar phase (a proxy for the spring-neap tidal cycle), lunar cycle, and diel cycle. For all individuals two states (or regimes) were always selected (the autoregressive order was either three or four), representing different regimes of animal activity, i.e., state 1 for resting or horizontal swimming or slow vertical movement; state 2 for fast ascending and descending. The cycle of the four lunar phases was the only environmental covariate that explained the hidden state dynamics in all individuals, whereas lunar cycle was selected for two individuals and diel cycle for one only. 4. The method is an efficient approach to fit one-dimensional tag data using categorical environmental covariates, and to classify the observations into a small number of states representing individual behaviours of tagged individuals.
Datasets for optical tweezer autoregressive hidden Markov modeling
<p>Data to regenerate figures and tables analyzing optical tweezer data with arHMM. OT_arHMM.zip contains the ot_arhmm library needed to analyze these files. scripts.zip contains all the jupyter notebooks used to analyze data and make figures and tables. data.zip contains the raw data and processed_data.zip contains data that has already been put through the HMMs for analysis. These processed files can be generated from the raw data by running the analyze_all.ipynb notebook. All other notebooks require processed files to be present before running.</p>
High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
<p>Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of<br> human electrophysiological data using fMRI priors"</p> <p>https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1</p>
Data from: Markov switching autoregressive models for interpreting vertical movement data with application to an endangered marine apex predator
Open the record for dataset details and reuse information.
Figure 7 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 7. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/ km2 in log scale are mapped, S522c1 (A), S522c2 (B).
Figure S3 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S3. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/km2 in log scale are mapped, S782c1 (A), S782c2 (B), S1043sc1 (C), S1043sc2 (D), S1043sc3 (E).
Figure 6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 6. – Absolute values of spatial-temporal hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values in percentage (red). The light grey numbers represent the edge number of the tree.
Figure 1. – Eastern English Channel spatial grid using a in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 1. – Eastern English Channel spatial grid using a triangular mesh at a 522 km2 (A), 782 km2 (B) and 1043 km2 (C) average scale with the geographic coordinates in WGS84 of all the English Channel groundfish hauls survey from 1995 to 2014 (blue). The red points are the vertices used to define the mesh.
Figure 10 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 10. – Alosa sp. from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure 4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 4. – Spatial hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed in percentage (red). The light grey numbers represent the edge number of the tree.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.