suggest_parameters: Suggest starting hyperparameters for one model

In forecastsuite: Multi-Model Time Series Forecasting Toolkit and Local App

Suggest starting hyperparameters for one model

Description

Derives starting-point hyperparameters from analyze_series()'s diagnostics rather than fitting a grid of candidates. These are informed starting points, not optimal values – for ARIMA/SARIMA, auto.arima() still searches the order space better than this heuristic; the suggested p/q matter mainly for hand-tuning, while d/D come straight from ndiffs()/nsdiffs().

Usage

suggest_parameters(analysis, model_key)

Arguments

analysis	The list returned by analyze_series().
model_key	A registry key, e.g. "prophet", "arima", "sarima". Models with no tunable parameters return an empty suggestion.

Value

A list with params (a named list of suggested argument values, matching that model's fit() parameters), text (a one-line human-readable summary), and reasons (a character vector explaining each suggestion).

Examples

df <- tibble::tibble(ds = as.Date("2024-01-01") + 0:59, y = 1:60 + rnorm(60))
suggest_parameters(analyze_series(df, "day"), "arima")

forecastsuite documentation built on Oct. 18, 2026, 5:08 p.m.