PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)
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Updated
Jul 10, 2024 - Jupyter Notebook
PAMI is a Python library containing 100+ algorithms to discover useful patterns in various databases across multiple computing platforms. (Active)
An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution
tsl: a PyTorch library for processing spatiotemporal data.
Bayesian Neural Field models for prediction in large-scale spatiotemporal datasets
[KDD 2022] Official implementation of "SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data".
Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.
The official repository for "Unveiling the Role of Climate in Spatially Synchronized Locust Outbreak Risks"
[AAAI] A spatiotemporal embedding framework for geographical entities
Preprocessing Scripts for VIC-CropSyst Modeling
Attention Feature Fusion base on spatial-temporal Graph Convolutional Network(AFFGCN)
Few graphs/plots and maps as outputs of movement ecology research (GPS telemetry)
Harmonize heterogenous spatiotemporal gridded agriculture-related datasets. Part of a larger ongoing project to monitor land and water use by combining irrigation and gridded data via remote sensing data with machine learning.
Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.
[KDD 2023] Official implementation of "Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM".
Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 2022)
Spatiotemporal variability in the association between mental illness and substance use mortality and unemployment in the contiguous US
Given a shapefile with time-annotated vector objects (e.g., building footprints + construction year), this script will automatically create an animated GIF illustrating the dynamics for a user-specified period of time
Spatial disparity in tract-level associations between mental illness mortality and greenspace exposure in the Pacific Northwest
This repository introduces Deep Particulate Matter Network with a Separated Input model based on deep learning by using ConvGRU, which can simultaneously analyze spatiotemporal information to consider the diffusion of particulate matter.
Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
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