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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 or Python 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

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

install.packages("forecastML")
library(forecastML)
devtools::install_github("nredell/forecastML")
library(forecastML)

Vignettes

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

Cheat Sheets

  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, dynamic, 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

Examples - R & Python

R

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))


R & Python

Now we’ll look at an example similar to above. The main difference is that our user-defined modeling and prediction functions are now written in Python. Thanks to the reticulate R package, entire ML workflows already written in Python can be imported into forecastML with the simple addition of 2 lines of R code.

library(forecastML)
library(reticulate)  # Move Python objects in and out of R. See the reticulate package for setup info.

reticulate::source_python("modeling_script.py")  # Run a Python file and import objects into R.


data("data_seatbelts", package = "forecastML")

horizons <- c(1, 12)  # 2 models that forecast 1 and 1:12 time steps ahead.

# A lookback across select time steps in the past. Feature lags 1 through 9 will be silently dropped from the 12-step-ahead model.
lookback <- c(1, 3, 6, 9, 12, 15)

date_frequency <- "1 month"  # Time step frequency.

# The date indices, which don't come with the stock dataset, should not be included in the modeling data.frame.
dates <- seq(as.Date("1969-01-01"), as.Date("1984-12-01"), by = date_frequency)

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

# Create 2 custom validation datasets for outer-loop nested cross-validation. The purpose of
# the multiple validation windows is to assess expected forecast accuracy for specific
# time periods while supporting an investigation of the hyperparameter stability for
# models trained on different time periods. Validation windows can overlap.
window_start <- c(as.Date("1983-01-01"), as.Date("1984-01-01"))
window_stop <- c(as.Date("1983-12-01"), as.Date("1984-12-01"))

windows <- forecastML::create_windows(data_train, window_start = window_start, window_stop = window_stop)


modeling_script.py

import pandas as pd
from sklearn import linear_model

# User-define model.
# A user-defined wrapper function for model training that takes the following
# arguments: (1) a horizon-specific pandas DataFrame made with create_lagged_df(..., type = "train")
# (e.g., my_lagged_df$horizon_h)
def py_model_function(data):
  
  X = data.iloc[:, 1:]
  y = data.iloc[:, 0]
  
  model_lasso = linear_model.Lasso(alpha = 0.1)
  
  model_lasso.fit(X = X, y = y)
  
  return(model_lasso)

# User-defined prediction function.
# The predict() wrapper function takes 2 positional arguments. First,
# the returned model from the user-defined modeling function (py_model_function() above).
# Second, a pandas DataFrame of model features. The function
# can return a 1- or 3-column pandas DataFrame with either (a) point
# forecasts or (b) point forecasts plus lower and upper forecast bounds (column order and names do not matter).
def py_prediction_function(model, data_x):
  
  data_pred = pd.DataFrame({'y_pred': model.predict(data_x)})
  
  return(data_pred)


# Train a model across forecast horizons and validation datasets.
model_results <- forecastML::train_model(data_train,
                                         windows = windows,
                                         model_name = "LASSO",
                                         model_function = py_model_function,
                                         use_future = FALSE)

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

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


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

# Forecasts.
data_forecasts <- predict(model_results, prediction_function = list(py_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 = dates[-(1:150)], horizons = c(1, 12))


Roadmap