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codez

codez fits a seq2seq encoder-decoder model for time-feature forecasting. Version 2.0 moves the neural backend from TensorFlow/Keras to torch, keeps the original codez() entry point, and adds a more R-native workflow through fit_codez().

Install backend

torch is used at fit time:

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

Basic use

library(codez)

model <- fit_codez(
  amzn_aapl_fb[, -1],
  dates = as.Date(amzn_aapl_fb$Date),
  seq_len = 20,
  n_samp = 3,
  n_windows = 5,
  control = codez_control(epochs = 50, batch_size = 32, n_sim = 1000)
)

forecast <- predict(model)
summary(model)
plot(model)

What changed in 2.0

• Torch backend for the autoencoder and latent forward network.
• fit_codez() returns a codez_model with tidy forecasts, baseline backtests, and S3 methods.
• Convenience dependencies were replaced with local support functions; model fitting now only needs torch at runtime.
• codez() still returns the legacy list shape: history, best_model, and time_log.
• Mixed numeric/categorical data is rejected explicitly. Pass all numeric or all factor/character columns.
• Numeric fits include rolling naive and average baseline comparisons.

Output shape

predict(model) returns one row per feature and horizon, with forecast summary columns such as min, interval quantiles, 50%, mean, sd, and pred_scores.

The legacy result is still available at:

model$result

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.