The goal of ngboostForecast is to provide a tools for probabilistic forecasting by using Python’s ngboost for R users.
You can install the released version of ngboostForecast from CRAN with:
install.packages("ngboostForecast")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("Akai01/ngboostForecast")This is a basic example which shows you how to solve a common problem:
library(ngboostForecast)
#> Loading required package: reticulate
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
train = window(seatbelts, end = c(1983,12))
test = window(seatbelts, start = c(1984,1))
# without external variables with Ridge regression
model <- NGBforecast$new(Dist = Dist("LogNormal"),
Base = sklearner(module = "linear_model",
class = "Ridge"),
Score = Scores("LogScore"),
natural_gradient = TRUE,
n_estimators = 200,
learning_rate = 0.1,
minibatch_frac = 1,
col_sample = 1,
verbose = TRUE,
verbose_eval = 5,
tol = 1e-5)
model$fit(y = train[,2],
seasonal = TRUE,
max_lag = 12,
early_stopping_rounds = 10L)
fc <- model$forecast(h = 12, level = c(99,95,90, 80, 70, 60),
data_frame = FALSE)
autoplot(fc) + autolayer(test[,2])
library(ngboostForecast)
dists <- list(Dist("Normal"))
base_learners <- list(sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 1),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 2),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 3),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 4),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 5),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 6),
sklearner(module = "tree", class = "DecisionTreeRegressor",
max_depth = 7))
scores <- list(Scores("LogScore"))
model <- NGBforecastCV$new(Dist = dists,
Base = base_learners,
Score = scores,
natural_gradient = TRUE,
n_estimators = list(10, 100),
learning_rate = list(0.1, 0.2),
minibatch_frac = list(0.1, 1),
col_sample = list(0.3),
verbose = FALSE,
verbose_eval = 100,
tol = 1e-5)params <- model$tune(y = AirPassengers,
seasonal = TRUE,
max_lag = 12,
xreg = NULL,
early_stopping_rounds = NULL,
n_splits = 4L)params
#> $ngboost_best_params
#> $ngboost_best_params$Base
#> DecisionTreeRegressor(max_depth=7.0)
#>
#> $ngboost_best_params$Dist
#> <class 'ngboost.distns.normal.Normal'>
#>
#> $ngboost_best_params$Score
#> <class 'ngboost.scores.LogScore'>
#>
#> $ngboost_best_params$col_sample
#> [1] 0.3
#>
#> $ngboost_best_params$learning_rate
#> [1] 0.2
#>
#> $ngboost_best_params$minibatch_frac
#> [1] 1
#>
#> $ngboost_best_params$n_estimators
#> [1] 100
#>
#>
#> $ngb_forecast_params
#> $ngb_forecast_params$seasonal
#> [1] TRUE
#>
#> $ngb_forecast_params$max_lag
#> [1] 12
#>
#> $ngb_forecast_params$K
#> [1] 5