Getting started with proteus

proteus fits a variational sequence-to-sequence model to one or more time features and returns forecasts, uncertainty summaries, diagnostic plots, and error metrics. Version 2.0 keeps the neural network and plotting dependencies small; optional packages are loaded only when their feature is requested.

A first forecast

The package includes amzn_aapl_fb, a data frame with daily prices and a date column. A compact run is:

library(proteus)
fit <- proteus(amzn_aapl_fb, target = "AMZN", dates = "Date",
               past = 30, future = 10, epochs = 5,
               future_plan = "future::sequential", verbose = FALSE)
fit$prediction$AMZN

The prediction table contains quantiles, location and scale summaries, and distribution diagnostics. fit$plot$AMZN visualizes the historical series and forecast interval, while fit$features_errors reports back-test metrics.

Optional features

Set smoother = TRUE or use omit = FALSE with missing values to opt into fANCOVA or imputeTS, respectively. Parallel cross-validation can be enabled with future_plan = "future::multisession" after installing future and furrr. The default sequential plan works with the core dependencies.