package::forecastML

The purpose of forecastML is to provide a series of functions and visualizations that simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. It's a wrapper package aimed at providing maximum flexibility in model-building--choose any machine learning algorithm from any R package--while helping the user quickly assess the (a) accuracy, (b) stability, and (c) generalizability of grouped (i.e., multiple related time series) and ungrouped single-outcome forecasts produced from potentially high-dimensional modeling datasets.

This package is inspired by Bergmeir, Hyndman, and Koo's 2018 paper A note on the validity of cross-validation for evaluating autoregressive time series prediction. In particular, forecastML makes use of

• lagged, grouped, dynamic, and static features,
• simple wrapper functions that support models from any R package,
• nested cross-validation with (a) user-specified standard cross-validation in the inner loop and (b) block-contiguous validation

datasets in the outer loop, and

• parallel processing with the future package

to build and evaluate high-dimensional forecast models without having to use methods that are time series specific.

The following quote from Bergmeir et al.'s article nicely sums up the aim of this package:

"When purely (non-linear, nonparametric) autoregressive methods are applied to forecasting problems, as is often the case (e.g., when using Machine Learning methods), the aforementioned problems of CV are largely irrelevant, and CV can and should be used without modification, as in the independent case."

Install

devtools::install_github("nredell/forecastML")
library(forecastML)

• Setting the following R environment parameter may be needed to compile the vignettes.

base::Sys.setenv(LC_ALL = "en_US.UTF-8")

Vignettes

The main functions covered in each vignette are shown below as function().

• Detailed forecastML overview vignette.

create_lagged_df(), create_windows(), train_model(), return_error(), return_hyper()

• Creating custom feature lags for model training. create_lagged_df(lookback_control = ...)

• Forecasting with multiple or grouped time series.

fill_gaps(), create_lagged_df(dates = ..., dynamic_features = ..., groups = ..., static_features = ...), create_windows(), train_model()

• Customizing the user-defined wrapper functions.

train() and predict()

Cheat Sheet

Key functions

1. fill_gaps: Optional if no temporal gaps/missing rows in data collection. Fill gaps in data collection and

prepare a dataset of evenly-spaced time series for modeling with lagged features. Returns a 'data.frame' with missing rows added in so that you can either (a) impute, remove, or ignore NAs prior to the forecastML pipeline or (b) impute, remove, or ignore them in the user-defined modeling function--depending on the NA handling capabilities of the user-specified model.

2. create_lagged_df: Create model training and forecasting datasets with lagged, grouped, and static features.

3. create_windows: Create time-contiguous validation datasets for model evaluation.

4. train_model: Train the user-defined model across forecast horizons and validation datasets.

5. return_error: Compute forecast error across forecast horizons and validation datasets.

6. return_hyper: Return user-defined model hyperparameters across validation datasets.

FAQ

• Q: Where does forecastML fit in with respect to popular R machine learning packages like mlr3 and caret?
• A: The idea is that forecastML takes care of the tedious parts of forecasting with ML methods: creating training and forecasting datasets with different

types of features--grouped, static, and dynamic--as well as simplifying validation dataset creation to assess model performance at specific points in time. That said, the workflow for packages like mlr3 and caret would mostly occur inside of the user-supplied modeling function which is passed into forecastML::train_model(). Refer to the wrapper function customization vignette for more details.

Example

Below is an example of how to create 12 horizon-specific ML models to forecast the number of DriversKilled 12 time periods into the future using the Seatbelts dataset. Notice in the last plot that there are multiple forecasts; these are from the slightly different LASSO models trained in the nested cross-validation. An example of selecting optimal hyperparameters and retraining to create a single forecast model (i.e., create_windows(..., window_length = 0)) can be found in the overview vignette.

library(glmnet)
library(forecastML)

# Sampled Seatbelts data from the R package datasets.
data("data_seatbelts", package = "forecastML")

# Example - Training data for 12 horizon-specific models w/ common lags per feature. The data do 
# not have any missing rows or temporal gaps in data collection; if there were gaps, 
# we would need to use fill_gaps() first.
horizons <- 1:12  # 12 models that forecast 1, 1:2, 1:3, ..., and 1:12 time steps ahead.
lookback <- 1:15  # A lookback of 1 to 15 dataset rows (1:15 * 'date frequency' if dates are given).

#------------------------------------------------------------------------------
# Create a dataset of lagged features for modeling.
data_train <- forecastML::create_lagged_df(data_seatbelts, type = "train",
                                           outcome_col = 1, lookback = lookback,
                                           horizon = horizons)

#------------------------------------------------------------------------------
# Create validation datasets for outer-loop nested cross-validation.
windows <- forecastML::create_windows(data_train, window_length = 12)

#------------------------------------------------------------------------------
# User-define model - LASSO
# A user-defined wrapper function for model training that takes the following
# arguments: (1) a horizon-specific data.frame made with create_lagged_df(..., type = "train")
# (e.g., my_lagged_df$horizon_h) and, optionally, (2) any number of additional named arguments
# which can also be passed in '...' in train_model(). The function returns a model object suitable for 
# the user-defined predict function. The returned model may also be a list that holds meta-data such 
# as hyperparameter settings.

model_function <- function(data, my_outcome_col) {  # my_outcome_col = 1 could be defined here.

  x <- data[, -(my_outcome_col), drop = FALSE]
  y <- data[, my_outcome_col, drop = FALSE]
  x <- as.matrix(x, ncol = ncol(x))
  y <- as.matrix(y, ncol = ncol(y))

  model <- glmnet::cv.glmnet(x, y)
  return(model)  # This model is the first argument in the user-defined predict() function below.
}

#------------------------------------------------------------------------------
# Train a model across forecast horizons and validation datasets.
# my_outcome_col = 1 is passed in ... but could have been defined in the user-defined model function.
model_results <- forecastML::train_model(data_train,
                                         windows = windows,
                                         model_name = "LASSO", 
                                         model_function = model_function,
                                         my_outcome_col = 1,  # ...
                                         use_future = FALSE)

#------------------------------------------------------------------------------
# User-defined prediction function - LASSO
# The predict() wrapper function takes 2 positional arguments. First,
# the returned model from the user-defined modeling function (model_function() above).
# Second, a data.frame of model features. If predicting on validation data, expect the input data to be 
# passed in the same format as returned by create_lagged_df(type = 'train') but with the outcome column 
# removed. If forecasting, expect the input data to be in the same format as returned by 
# create_lagged_df(type = 'forecast') but with the 'index' and 'horizon' columns removed. The function 
# can return a 1- or 3-column data.frame with either (a) point
# forecasts or (b) point forecasts plus lower and upper forecast bounds (column order and names do not matter).

prediction_function <- function(model, data_features) {

  x <- as.matrix(data_features, ncol = ncol(data_features))

  data_pred <- data.frame("y_pred" = predict(model, x, s = "lambda.min"),  # 1 column is required.
                          "y_pred_lower" = predict(model, x, s = "lambda.min") - 50,  # optional.
                          "y_pred_upper" = predict(model, x, s = "lambda.min") + 50)  # optional.
  return(data_pred)
}

# Predict on the validation datasets.
data_valid <- predict(model_results, prediction_function = list(prediction_function), data = data_train)

#------------------------------------------------------------------------------
# Plot forecasts for each validation dataset.
plot(data_valid, horizons = c(1, 6, 12))

#------------------------------------------------------------------------------
# Forecast.

# Forward-looking forecast data.frame.
data_forecast <- forecastML::create_lagged_df(data_seatbelts, type = "forecast",
                                              outcome_col = 1,
                                              lookback = lookback, horizons = horizons)

# Forecasts.
data_forecasts <- predict(model_results, prediction_function = list(prediction_function),
                          data = data_forecast)

# We'll plot a background dataset of actuals as well.
plot(data_forecasts, data_actual = data_seatbelts[-(1:150), ], 
     actual_indices = as.numeric(row.names(data_seatbelts[-(1:150), ])), 
     horizons = c(1, 6, 12), windows = c(5, 10, 15))

Roadmap

• The following outlines what I'd like to improve leading up to an eventual CRAN release.
  • Additional testing with R package testthat.