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lambdaTS 2.0: probabilistic multivariate forecasting

Giancarlo Vercellino

Overview

lambdaTS fits a variational sequence-to-sequence model for jointly forecasting multiple numeric time series. Version 2.0 keeps the original lambdaTS() API, uses internal preprocessing helpers, and produces predictive samples and interval summaries.

Forecasting

library(lambdaTS)

result <- lambdaTS(
  data = bitcoin_gold_oil,
  target = c("gold_close", "oil_Close"),
  future = 10,
  past = 30,
  deriv = 1,
  epochs = 5,
  sample_n = 50,
  seed = 42
)

The returned prediction list contains horizon-by-horizon quantiles, means, standard deviations, minima, and maxima. feature_errors reports validation metrics on the original scale, while history and plot provide visual diagnostics.

Reproducibility

Set seed for reproducible preprocessing and torch initialization. The model can use dev = "cuda" when a compatible torch installation and GPU are available; CPU is the default.

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.