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forecastsuite

A local, full-featured companion to the hosted Forecast Dashboard Shiny app. Where the hosted app deliberately sticks to Prophet + ARIMA to fit a ~1GB-RAM free hosting tier, this package has no such constraint and adds:

• A pluggable model registry (register_model()/get_model()/list_models()) — every model implements a fit/forecast/to_tibble contract, so adding a new model later is one file + one register_model() call.
• Built-in models: Prophet (optional, requires prophet), ARIMA, SARIMA (both with auto and manual p,d,q/P,D,Q order entry), ETS, TBATS, NNETAR, Holt-Winters, and LSTM (optional, requires torch).
• A bundled local Shiny app (run_app()) with all models exposed, Plotly always on, and a dataset-driven model recommendation step.
• Import from any of four sources: a file upload (CSV, TSV or Excel – multi-sheet workbooks let you pick the worksheet), a data frame already in your R global environment (or, esquisse-style, a built-in dataset from any installed package – e.g. datasets, ggplot2 – picked by package then dataset name, no library() or data() call needed first), a link-shared Google Sheet, a CSV URL, or pasted delimited text.
• Dates split across columns (Year + Quarter, Year + Month, optionally + Day) are assembled automatically, and aggregation is set to the finest part supplied.
• An N-way model comparison: overlay several models’ forecasts on one Plotly chart alongside the side-by-side metrics table.
• Population normalization: divide by a population table to forecast incidence (e.g. cases per 100,000) rather than absolute counts.
• A full holiday system: Sundays, a fixed-date catalog, movable holidays from a file, manual entries, relabel/remove, per-holiday windows, and a consistency check against your data.
• Multi-window evaluation (Train / Last 6 Months / Last 2 Years / held-out Test) and suggested starting hyperparameters (including ARIMA/SARIMA’s p, d, q, P, D, Q, all data-derived) alongside the model recommendation.
• Individual Observations as an alternative to Aggregated data: import one row per event (e.g. one case, one visit) and it’s counted into periods for you, no value column needed. Population normalization works with either format.
• A Model Guide tab explaining every model, the parameters, the metrics, and how the recommendation heuristic works.
• A “Show Code” panel that prints runnable R reproducing whatever you just fit or compared, syntax-highlighted, downloadable as a .R file, or copyable to the clipboard in one click.
• An Analysis panel (Model tab): seasonal decomposition (trend/seasonal/remainder), anomaly detection on the raw series (IQR or z-score, downloadable as CSV), residual diagnostics after a fit (Ljung-Box, Shapiro-Wilk, residual ACF, and prediction interval coverage – is the error actually unpredictable noise, or is there structure left over, and are the uncertainty bounds honest?), bias/drift tracking (is the model’s error systematically one-sided, and is it growing across the test window?), and, with a grouping column active, a cross-group correlation heatmap/table. Decomposition, anomaly detection, and bias/drift need only a finalized dataset or a fit, no extra setup.
• Forecast ensembling: combine two or more compared models’ forecasts (simple average or inverse-error-weighted) into one more entry on the same comparison plot/table – “Build Ensemble from Selected” on the Model tab.
• A multi-model backtest leaderboard: the same rolling-origin folds as Cross-Validation, but across every model selected for Compare Models at once, ranked by mean test MASE.
• Plot appearance controls (trend/uncertainty/holiday/changepoint toggles, colour pickers for every line) and one-click downloads: the processed dataset and the holiday list as CSV, and the forecast plot – single-model or the multi-model comparison – as PNG.
• A bundled 5-year demo dataset (simulated daily clinic visits, with missing and partially-open Sundays) so you can explore the whole app with no data of your own – pick it from the same icon row as the other import sources.
• An optional grouping column (District, Sex, Product, …): split your dataset into one time series per distinct value instead of only ever seeing the aggregate, fit one model across every included group, browse each group’s own plot/metrics, and compare them on one overlay plot (actuals + forecasts, one color per group). Population normalization, the recommendation table, and the holiday consistency check are all group-aware when it’s on. Messy raw values (“female”/“Female”/ “FEMALE”) are auto-merged by casing/spacing, plus a manual relabel table for anything else (abbreviations, typos) – the same select-and-relabel pattern used for holidays.
• Bottom-up hierarchical reconciliation: once you’ve fit two or more groups, view a “Reconciled (bottom-up)” forecast – the sum of the already-fit group forecasts, always coherent by construction, labeled as partial if not every configured group was fit this run.
• Rolling-origin cross-validation: refit the currently-viewed series at several walk-forward cutoffs instead of relying on one train/test split, for a more robust read on accuracy (Model tab, “Run Cross-Validation”).
• Save your dataset(s), holidays, and every setting as one project file, and reload it in a fresh session with no re-import needed – fitted results aren’t saved, so you re-click Fit & Forecast after loading.

Install

install.packages("remotes")
remotes::install_local("forecastsuite")

Run the app

forecastsuite::run_app()

In RStudio, the app is also available from the Addins menu (“Run forecastsuite”) – no need to type the command.

Learn more

vignette("forecastsuite") walks through the bundled app tab-by-tab and the model registry API for scripting forecasts directly in R, without the app.

Prophet support (optional)

install.packages("prophet")

Everything else works without prophet — list_models() simply omits Prophet until it’s installed. prophet pulls in a compiled Stan backend (rstan/StanHeaders) that has a real history of install/check fragility on some platforms, so it’s kept optional (Suggests, not Imports) rather than force-loaded by library(forecastsuite). Once installed, Prophet’s changepoint/seasonality/holiday priors are exposed on the Model tab like any other model’s parameters, saved/restored with a project file, and included in the “Show Code” panel.

LSTM support (optional)

install.packages("torch")
torch::install_torch()

Everything else works without torch — list_models() simply omits LSTM until it’s installed. Once available, its epochs / hidden units / lookback window / learning rate are exposed on the Model tab like any other model’s parameters, saved/restored with a project file, and included in the “Show Code” panel.

Documentation (contributors)

Every exported function has real roxygen2 comments in its R/ source, but NAMESPACE/man/*.Rd are regenerated from them, not hand-edited. After changing a function’s documentation or @export status:

Rscript document.R

(installs roxygen2 if needed, then runs roxygen2::roxygenise()). Commit the updated NAMESPACE/man/.

Relationship to the hosted app

R/data_utils.R, R/metric_utils.R, R/holidays_helpers.R, and R/horizon_utils.R are adapted from the parent repo’s helpers/ files (same behavior, package-namespaced). They are intentionally copied, not shared, so this package and the hosted app can evolve independently without one destabilizing the other. If you fix a bug in one copy, check whether the other needs the same fix.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.