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.
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.
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.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.