Deep Learning for Time Series, simplified.
Important: This package is exprimental. Functions may change until the package matures.
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.
Using deep_ar()
, which connects to GluonTS DeepAREstimator()
.
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 = 36,
epochs = 10,
num_batches_per_epoch = 50,
learn_rate = 0.001,
num_layers = 2,
dropout = 0.10
) %>%
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)
modeltime.gluonts
is currently available on GitHub only. Not on CRAN
yet.
remotes::install_github("business-science/modeltime.gluonts")
Important: Use install_gluonts()
to set up the “r-gluonts”
python
environment used by modeltime.gluonts
. You only need to do
this once, when you first set up the package.
# GluonTS Installation
# - This sets up the Python Environment
# - Only need to run 1-time, then you're set!
install_gluonts()
My Talk on High-Performance Time Series Forecasting
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.
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I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:
Modeltime
- 30+
Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)GluonTS
(Competition Winners)Unlock the High-Performance Time Series Forecasting Course
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