knitr::opts_chunk$set( collapse = TRUE, message = F, warning = F, comment = "#>", fig.path = "man/figures/README-", out.width = "90%", fig.align = "center" )
GluonTS Deep Learning in R.
Modeltime GluonTS integrates the Python GluonTS Deep Learning Library, making it easy to develop forecasts using Deep Learning for those that are comfortable with the Modeltime Forecasting Workflow.
knitr::include_graphics("man/figures/m4_hourly_forecast.jpg")
Important: This package is being maintained on GitHub (not CRAN). Please install the GitHub version, which is updated with the latest features:
# Install GitHub Version remotes::install_github("business-science/modeltime.gluonts") # Install Python Dependencies modeltime.gluonts::install_gluonts()
For more detailed installation instructions and troubleshooting guidance, visit our Installation Guide.
Make your first deep_ar()
model, which connects to the GluonTS DeepAREstimator()
. For a more detailed walkthough, visit our Getting Started Guide.
library(modeltime.gluonts) library(tidymodels) library(tidyverse) # Fit a GluonTS DeepAR Model model_fit_deepar <- deep_ar( id = "id", freq = "M", prediction_length = 24, lookback_length = 48, epochs = 5 ) %>% set_engine("gluonts_deepar") %>% fit(value ~ ., training(m750_splits)) # Forecast with 95% Confidence Interval modeltime_table( model_fit_deepar ) %>% modeltime_calibrate(new_data = testing(m750_splits)) %>% modeltime_forecast( new_data = testing(m750_splits), actual_data = m750, conf_interval = 0.95 ) %>% plot_modeltime_forecast(.interactive = FALSE)
knitr::include_graphics("man/figures/deepar_example_1.png")
Learn a growing ecosystem of forecasting packages
knitr::include_graphics("man/figures/modeltime_ecosystem.jpg")
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
Become the forecasting expert for your organization
High-Performance Time Series Course
Time series is changing. Businesses now need 10,000+ time series forecasts every day. This is what I call a High-Performance Time Series Forecasting System (HPTSF) - Accurate, Robust, and Scalable Forecasting.
High-Performance Forecasting Systems will save companies by improving accuracy and scalability. Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).
I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:
Modeltime
- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)GluonTS
(Competition Winners)Become the Time Series Expert for your organization.
Take the High-Performance Time Series Forecasting Course
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