neuralprophet

The NEW Neural Prophet arrives to R!

# Install Development Version 
remotes::install_github("AlbertoAlmuinha/neuralprophet")

Not on CRAN yet:

#install.packages("neuralprophet")

Neural Prophet Algorithm

Using neural_prophet(), which connects to Python NeuralProphet().

library(neuralprophet)
library(tidymodels)
library(tidyverse)
library(timetk)

m <- m4_monthly %>% filter(id == "M750")

splits <- initial_time_split(m)


model_fit_nprophet <- neural_prophet(
    freq            = "M",
    growth          = "linear",
    trend_reg       = 3,
    learn_rate      = 0.1,
    changepoint_range = 0.8,
    seasonality_mode = "additive"
) %>%
    set_engine("prophet") %>%
    fit(value ~ ., training(splits))

# Forecast with 95% Confidence Interval
modeltime_table(
    model_fit_nprophet
) %>%
    modeltime_calibrate(new_data = testing(splits)) %>%
    modeltime_forecast(
        new_data      = testing(splits),
        actual_data   = m,
        conf_interval = 0.95
    ) %>%
    plot_modeltime_forecast(.interactive = FALSE)

Installation

There are 2 key components to installing neuralprophet:

1. Download the R-Package, neuralprophet. This installs the R-Bindings, which allows you to interface with NeuralProphet

2. Set up the Python Environment so neuralprophet can connect to the neuralprophet python package.

Step 1 - Download & Install neuralprophet

Download the latest development version.

remotes::install_github("AlbertoAlmuinha/neuralprophet")

Step 2 - Python Environment Setup

Next, you’ll need to set up a Python Environment that contains at a minimum:

• neuralprophet==0.2.7
• pillow==8.3.0
• pandas==1.2.5
• numpy
• pytorch==1.6

The python environment then needs to be activated with reticulate.

Fast Setup

You can use install_nprophet() to prepare and bind to a python environment containing neuralprophet and the required python packages.

• You only need to run this one time, and then you are good to go.
• Each time you load neuralprophet, the package will include this environment in it’s search process.

# Neural Prophet Installation - Run 1st time
install_nprophet()

Step 3 - Restart R and Try an Example

Restart your R session (if in RStudio, close and re-open). Then try this example.

library(neuralprophet)
library(tidymodels)
library(tidyverse)
library(timetk)

m <- m4_monthly %>% filter(id == "M750")

splits <- initial_time_split(m)


model_fit_nprophet <- neural_prophet(
    freq            = "M",
    growth          = "linear",
    trend_reg       = 3,
    learn_rate      = 0.1,
    changepoint_range = 0.8,
    seasonality_mode = "additive"
) %>%
    set_engine("prophet") %>%
    fit(value ~ ., training(splits))

# Forecast with 95% Confidence Interval
modeltime_table(
    model_fit_nprophet
) %>%
    modeltime_calibrate(new_data = testing(splits)) %>%
    modeltime_forecast(
        new_data      = testing(splits),
        actual_data   = m,
        conf_interval = 0.95
    ) %>%
    plot_modeltime_forecast(.interactive = FALSE)

Troubleshooting Installation

Python Environment setup is always fun. Here are a few recommendations if you run into an issue.

• Check to make sure Conda or Miniconda is available using reticulate::conda_version(). If no conda version is returned, then use reticulate::install_miniconda() to install Miniconda (recommended vs full Aniconda). Then (re-)run install_gluonts().

• Check if Neural Prophet (Python) is available using reticulate::py_module_available("neuralprophet"). If this returns TRUE, then your installation has succeeded in building the environment, but you may have other issues like missing C++ build tools (next).

• Windows 10 error: Microsoft Visual C++ is required. Here are the instructions for installing the C++ tools needed.

• Other installation issues. Please file a GitHub issue here.