temporal_multiscale: Multi-scale temporal structure

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Multi-scale temporal structure

Description

Specify a temporal random effect that decomposes variation into separate scales: a smooth trend, an optional seasonal cycle, and a short-term component. Each scale uses its own prior, letting slow and fast dynamics be modelled jointly.

Usage

temporal_multiscale(
  time_var,
  trend = c("rw2", "rw1", "none"),
  seasonal = NULL,
  short_term = c("ar1", "iid", "none"),
  group_var = NULL,
  shared = NULL
)

## S3 method for class 'tulpa_temporal_multiscale'
print(x, ...)

Arguments

time_var	Single character string naming the time variable in the data.
trend	Prior for the smooth long-term trend. One of "rw2", "rw1", or "none".
seasonal	Optional integer period (⁠>= 2⁠) of a seasonal cycle, e.g. 12 for monthly data with an annual cycle. NULL (default) omits the seasonal component.
short_term	Prior for the short-term component. One of "ar1", "iid", or "none".
group_var	Optional character string naming a grouping variable for group-specific temporal effects.
shared	Whether the effect is shared across processes in a multi-process model. NULL (default) shares it; FALSE fits process-specific effects and emits a warning.
x	A tulpa_temporal_multiscale specification.
...	Ignored.

Details

At least one of trend, seasonal, or short_term must be active.

Value

A tulpa_temporal_multiscale object.

See Also

temporal_rw1(), temporal_rw2(), temporal_ar1() for single-scale temporal priors.

Examples

# Trend + annual seasonal cycle + AR1 short-term component on monthly data
temporal_multiscale("month", trend = "rw2", seasonal = 12, short_term = "ar1")

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.