Type: Package
Title: Temporal Encoder-Masked Probabilistic Ensemble Regressor
Version: 1.1.0
Maintainer: Giancarlo Vercellino <giancarlo.vercellino@gmail.com>
Description: Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: torch (≥ 0.11.0), purrr (≥ 1.0.1), imputeTS (≥ 3.3), lubridate (≥ 1.9.2), ggplot2 (≥ 3.5.1), scales (≥ 1.3.0)
URL: https://rpubs.com/giancarlo_vercellino/temper
Suggests: knitr, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Depends: R (≥ 4.1.0)
NeedsCompilation: no
Packaged: 2025-08-18 10:55:44 UTC; gianc
Author: Giancarlo Vercellino [aut, cre, cph]
Repository: CRAN
Date/Publication: 2025-08-18 11:20:02 UTC

Temporal Encoder–Masked Probabilistic Ensemble Regressor

Description

Temper trains and deploys a hybrid forecasting model that couples a temporal auto-encoder (shrinks a sliding window of length 'past' into a latent representation of size 'latent_dim') and a masked neural decision forest (an ensemble of 'n_trees' soft decision trees of depth 'depth'; feature-level dropout is governed by 'init_prob' and annealed by a Gumbel–Softmax with parameter 'temperature') and a CRPS loss (Continuous Ranked Probability Score) that blends the probabilistic forecasting error with a reconstruction term ('lambda_rec × MSE'), to yield multi-step probabilistic forecasts and their fan chart. Model weights are optimized with ADAM or other options, optional early stopping.

Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.

Usage

temper(
  ts,
  future,
  past,
  latent_dim,
  n_trees = 30,
  depth = 6,
  init_prob = 0.8,
  temperature = 0.5,
  n_bases = 10,
  train_rate = 0.7,
  epochs = 30,
  optimizer = "adam",
  lr = 0.005,
  batch = 32,
  lambda_rec = 0.3,
  patience = 15,
  verbose = TRUE,
  alpha = 0.1,
  dates = NULL,
  seed = 42
)

Arguments

ts

Numeric vector of length at least past + future. Represents the input time series in levels (not log-returns). Missing values are automatically imputed using na_kalman.

future

Integer \geq 1. Forecast horizon: the number of steps ahead to predict.

past

Integer \geq 1. Length of the sliding window used to feed the encoder.

latent_dim

Integer \geq 1. Dimensionality of the autoencoder's latent bottleneck.

n_trees

Integer \geq 1. Number of trees in the neural decision forest ensemble. Usually in the range of 30 to 200. Default: 30.

depth

Integer \geq 1. Depth of each decision tree (i.e., number of binary splits). Usually in the range of 4 to 12. Default: 6.

init_prob

Numeric in (0, 1). Initial probability that each input feature is kept by the feature mask (used for stochastic feature selection). A value of 0 means always dropped; 1 means always included. Default: 0.8.

temperature

Positive numeric. Temperature parameter for the Gumbel–Softmax distribution used during feature masking. Lower values lead to harder (closer to binary) masks; higher values encourage smoother gradients. Default: 0.5.

n_bases

Integer \geq 1. Max numbers of bases for the Gaussian mixture. Default: 10.

train_rate

Numeric in (0, 1). Proportion of samples allocated to the training set. The remaining samples form the validation set used for early stopping. Default: 0.7.

epochs

Positive integer. Maximum number of training epochs. Have a look at the loss plot to decide the right number of epochs. Default: 30.

optimizer

Character string. Optimizer to use for training (adam, adamw, sgd, rprop, rmsprop, adagrad, asgd, adadelta). Default: adam.

lr

Positive numeric. Learning rate for the optimizer. Default: 0.005.

batch

Positive integer. Mini-batch size used during training. Default: 32.

lambda_rec

Non-negative numeric. Weight applied to the reconstruction loss relative to the probabilistic CRPS forecasting loss. Default: 0.3.

patience

Positive integer. Number of consecutive epochs without improvement on the validation CRPS before early stopping is triggered. Default: 15.

verbose

Logical. If TRUE, prints CRPS values for each epoch during training. Default: TRUE.

alpha

Numeric in (0, 1). Confidence level used to define the predictive interval band width in the output fan chart. Default: 0.1.

dates

Optional Date vector of the same length as ts. If supplied, fan chart x-axes use calendar dates; otherwise, integer time indices are used. Default: NULL.

seed

Optional integer. Used to seed both R and Torch random number generators for reproducibility. Default: 42.

Value

A named list with four components

'loss'

A ggplot in which training and validation CRPS are plotted against epoch number, useful for diagnosing over-/under-fitting.

'pred_funs'

A length-'future' list. Each element contains four empirical distribution functions (pdf, cdf, icdf, sampler) created by empfun

'plot'

A ggplot object showing the historical series, median forecast and predictive interval. A print-ready fan chart.

'time_log'

An object measuring the wall-clock training time.

Author(s)

Maintainer: Giancarlo Vercellino giancarlo.vercellino@gmail.com [copyright holder]

See Also

Useful links:

Examples


set.seed(2025)
ts <- cumsum(rnorm(250))          # synthetic price series
fit <- temper(ts, future = 3, past = 20, latent_dim = 5, epochs = 2)

# 80 % predictive interval for the 3-step-ahead forecast
pfun <- fit$pred_funs$t3$pfun
pred_interval_80 <- c(pfun(0.1), pfun(0.9))

# Visual diagnostics
print(fit$plot)
print(fit$loss)

Tech Stock Time Series Dataset

Description

A multivariate dataset for closing prices for several major tech stocks over time. Source: YahooFinance.

Usage

data(dummy_set)

Format

A data frame with 2133 observations of 4 variables:

dates

Character vector of dates in "YYYY-MM-DD" format.

TSLA.Close

Numeric. Closing prices for Tesla.

MSFT.Close

Numeric. Closing prices for Microsoft.

MARA.Close

Numeric. Closing prices for MARA Holdings.

Examples

data(dummy_set)
plot(as.Date(dummy_set$dates), dummy_set$TSLA.Close, type = "l")

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