Ensemble Algorithms for Time Series Forecasting with Modeltime
modeltime extension that implements ensemble forecasting methods including model averaging, weighted averaging, and stacking.
Install the CRAN version:
Or, install the development version:
Load the following libraries.
Create a Modeltime Table using the
m750_models #> # Modeltime Table #> # A tibble: 3 × 3 #> .model_id .model .model_desc #> <int> <list> <chr> #> 1 1 <workflow> ARIMA(0,1,1)(0,1,1) #> 2 2 <workflow> PROPHET #> 3 3 <workflow> GLMNET
Then turn that Modeltime Table into a Modeltime Ensemble.
ensemble_fit <- m750_models %>% ensemble_average(type = "mean") ensemble_fit #> ── Modeltime Ensemble ─────────────────────────────────────────── #> Ensemble of 3 Models (MEAN) #> #> # Modeltime Table #> # A tibble: 3 × 3 #> .model_id .model .model_desc #> <int> <list> <chr> #> 1 1 <workflow> ARIMA(0,1,1)(0,1,1) #> 2 2 <workflow> PROPHET #> 3 3 <workflow> GLMNET
To forecast, just follow the Modeltime Workflow.
# Calibration calibration_tbl <- modeltime_table( ensemble_fit ) %>% modeltime_calibrate(testing(m750_splits), quiet = FALSE) # Forecast vs Test Set calibration_tbl %>% modeltime_forecast( new_data = testing(m750_splits), actual_data = m750 ) %>% plot_modeltime_forecast(.interactive = FALSE)
Learn a growing ecosystem of forecasting packages
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
Become the forecasting expert for your organization
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:
Time Series Machine Learning (cutting-edge) with
Modeltime- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
Deep Learning with
- 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.
Become the Time Series Expert for your organization.