Out-of-Sample time series forecasting is a common, important, and subtle task. The OOS package introduces a comprehensive and cohesive API for the out-of-sample forecasting workflow: data preparation, forecasting - including both traditional econometric time series models and modern machine learning techniques - forecast combination, model and error analysis, and forecast visualization.

The key difference between OOS and the other time series forecasting packages is that it operates out-of-sample by construction. That is, OOS functions are designed to re-clean data and re-train models each forecast date and are careful not to introduce look-ahead bias into its information set via data cleaning or forecasts via model training. This framework is in contrast to other packages which tend to focus on cleaning data or fitting a forecast model once, leaving the user to construct the out-of-sample exercise on their own.

Available tools and techniques are summarized below and may be reviewed under the Tools tab. While vignettes and other extended documentation of the OOS package’s capabilities may be found under the Workflow tab.

Workflow and available Tools

1. Prepare Data

Clean Outliers Impute Missing Data (via imputeTS) Dimension Reduction
Winsorize Linear Interpolation Principal Components
Trim Kalman Filter
Moving Average
Seasonal Decomposition

2. Forecast

Univariate Forecasts (via forecast) Multivariate Forecasts (via caret) Forecast Combinations
Random Walk Vector Autoregression Mean
ARIMA Linear Regression Median
ETS LASSO Regression Trimmed (Winsorized) Mean
Spline Ridge Regression N-Best
Theta Method Elastic Net Linear Regression
TBATS Principal Component Regression LASSO Regression
STL Partial Least Squares Regression Ridge Regression
AR Perceptron Random Forest Partial Egalitarian LASSO
Tree-Based Gradient Boosting Machine Principal Component Regression
Single Layered Neural Network Partial Least Squares Regression
Random Forest
Tree-Based Gradient Boosting Machine
Single Layered Neural Network

3. Analyze

Accuracy Compare Visualize
Mean Square Error (MSE) Forecast Error Ratios Forecasts
Root Mean Square Error (RMSE) Diebold-Mariano Test (for unnested models) Errors
Mean Absolute Error (MAE) Clark and West Test (for nested models)
Mean Absolute Percentage Error (MAPE)


If you should have questions, concerns, or wish to collaborate, please contact Tyler J. Pike