kzfs — Multi-Scale Motions Separation with Periodogram
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Updated
Aug 13, 2017 - R
kzfs — Multi-Scale Motions Separation with Periodogram
Modeling and visualizing hydrologic observations indexed in space and time. This work is heavily indebted to the piece from Wikle, Zammit-Mangion, and Cressie (2019) who are blazing the path to the summit for spatio-temporal statistics
Code and data from the "Spatial and Spatio-temporal Bayesian Models with R-INLA" book
A reproduced research on HIV/AIDS mortality in order to investigate how the model covariates poverty, income inequality and spatiotemporal effects influence the model fit.
Files for analysing spatio-temporal plant species presence trends using approximate Bayesian inference with integrated nested Laplace approximations for presence-only data
In this work, we stratify malaria cases across Malawi to sub-district level, quantify spatio-temporal patterns and examined the effects of climate, environment, and malaria intervention.
Predicting NFL play yardage from autoencoded player tracking data (2019)
LLM4HRS:A LLM-based Spatio-temporal Imputation Model for Highly-sparse Remote Sensing Data
Statistical analysis of Jersey City bike sharing data using spatio-temporal models.
A tool to quantify the spatio-temporal continuity of sparse data
Simulation of the emergence of hierarchical and enduring settlement patterns during the Middle Ages
Deep Spatio-Temporal Residual Networks for Mobile Location Data Prediction (instead of original Crowd Flow Prediction) - Modified ST-ResNet
Learning Spatio-Temporal Statistics with R book using the STRbook package.
Trajectory prediction by Bayesian learning of spatiotemporal velocity fields and hybrid Kalman filter
Exploring mobility patterns in US cities
[ICDE'21] Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)
Code repo for Spatio-Temporal Denoising Graph Autoencoder (STD-GAE)
A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)
[AAAI'22] Event-Aware Multimodal Mobility Nowcasting
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