In Akai01/ngboostForecast: Probabilistic Time Series Forecasting

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ngboostForecast

The goal of ngboostForecast is to provide a tools for probabilistic forecasting by using Python's ngboost for R users.

Installation

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")

Example

This is a basic example which shows you how to solve a common problem:

library(ngboostForecast)

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])

Tuning

Set the parameters:

``` {r example2, echo = TRUE}

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)

## Tune the model:
```r

params <- model$tune(y = AirPassengers,
                     seasonal = TRUE,
                     max_lag = 12,
                     xreg = NULL,
                     early_stopping_rounds = NULL,
                     n_splits = 4L)

Best parameters:

params

Akai01/ngboostForecast documentation built on Aug. 9, 2022, 1:10 a.m.