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.**

**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.

**High-Performance Forecasting Systems will save companies MILLIONS of dollars.** 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**. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

- Time Series Machine Learning (cutting-edge) with
`Modeltime`

- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more) - NEW - Deep Learning with
`GluonTS`

(Competition Winners) - Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter Tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- Scalable Forecasting - Forecast 1000+ time series in parallel
- and more.