AI & ML
dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process
arXiv stat.CO0 views
arXiv:2611.07096v1 Announce Type: new Abstract: dsp provides a unified state-space framework for modeling nonstationary time series using the Dynamic Shrinkage Process (DSP), a class of global-local shrinkage priors. DSP adaptively regularizes time-varying parameters to shrink noise while preserving abrupt changes and other local structure. The package supports Gaussian and count-data smoothing, changepoint detection, time-varying parameter regression, and volatility estimation, with posterior predictive draws available for missing-value imputation and forecasting. Nonstationary time series often contain periods of relative stability punctuated by abrupt changes in mean or variance. dsp offers a common interface for model specification, posterior computation, summarization, and visualization across models that were previously developed separately in the literature. Core functionality is implemented in C++ via Rcpp for computational efficiency. We describe the underlying models, package design, and the main functions for fitting, summarizing, and plotting posterior results.
Source：https://arxiv.org/abs/2611.07096PreviousKeep Video Evidence and Thumbnail Layout in Separate StatesNextmarp: Model-Averaged Inference for Parametric Renewal Processes in R
Related posts
Information Loss Is All You Need: $TC^0$ Pseudorandomness from Worst-Case Lattices
0arXiv cs.CR
Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection
0arXiv cs.CR
Backdoor Containment via Expert Quarantine and Shutdown in LLMs
0arXiv cs.CR
Persona-Model Collapse in Emergent Misalignment
0arXiv cs.CR
Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study
0arXiv cs.CR
When appearance and motion disagree: evaluating class-motion consistency against disguised uncrewed surface vessels
0arXiv cs.CR