Variance-Guided Time-Series Modeling for Temporal Risk Detection.
varGuidTS
Variance-Guided Time-Series Modeling for Temporal Risk Detection
varGuidTS fits a balanced panel ARX-GARCHX model with subject-specific intercepts, shared population coefficients, and joint $\ell_1$ penalization on the conditional mean and variance covariate vectors. The fitted object also exposes an exceedance-based risk score
$$ \hat\pi_{s,t}(c) = P!\left(Y_{s,t} > c ;\middle|; \mathcal{F}_{t-1}\right), $$
useful as a personalized, scale-normalized temporal risk score from high-frequency multimodal panel data such as wearable-sensor streams.
Installation
# from GitHub
install.packages("remotes")
remotes::install_github("zionwzz/variance-guided-risk-demo")
# or from a local source tree
remotes::install_local("path/to/varGuidTS")
Project URL: https://github.com/zionwzz/variance-guided-risk-demo
Quick start
library(varGuidTS)
set.seed(1)
sim <- simulate_scenario(scen = 2, S = 4, T = 60,
d_noise = 4, noise_kind = "ar1", seed = 1)
df <- sim[, c("s", "t", "y",
grep("^X|^Noise|^Xbin", names(sim), value = TRUE))]
fit <- lmvt(df, p = 1, q = 0, r = 1, s_ord = 1,
lambda_beta = 0.05, lambda_gamma = 0.05,
use_x_in_variance = TRUE, maxit = 8)
print(fit)
pred <- predict(fit, df, threshold = quantile(df$y, 0.9))
head(pred)
# Coefficient summary table
sm <- summary(fit, include_zero = FALSE, sort_by = "abs_estimate")
head(sm$table)
# Subject-specific thresholds for personalized exceedance probabilities
subject_cutoffs <- tapply(df$y, df$s, quantile, probs = 0.75, na.rm = TRUE)
pred_subject <- predict(fit, df, threshold = subject_cutoffs,
innov_g = TRUE, innov_t = TRUE, df_t = 8)
head(pred_subject)
The README quick start gives the main workflow; the simulation function can be used to generate built-in scenarios for examples, tests, and method demonstrations.
What the package provides
| Function | Purpose |
|---|---|
lmvt() | Fit penalized panel ARX–GARCHX |
predict.lmvt() | Conditional mean, variance, and exceedance probability |
summary.lmvt() | Model diagnostics and coefficient summary table |
simulate_scenario() | Generate panels under the four canonical scenarios |
Maintainer
Min Lu [email protected].
License
MIT.
Citation
Please cite the package repository or the CRAN package record when using varGuidTS.