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

**lagged, grouped, dynamic,**and**static features**,**simple wrapper functions that support models from any**,`R`

or`Python`

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.”

- CRAN

- Development

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()`

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`fill_gaps`

:`NA`

s 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.Create model training and forecasting datasets with lagged, grouped, dynamic, and static features.`create_lagged_df`

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

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

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

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

:

**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.

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))
```

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.

- The
`reticulate::source_python()`

function will run a .py file and import any objects into your`R`

environment. As we’ll see below, we’ll only be importing library calls and functions to keep our`R`

environment clean.

```
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.
```

- Below is a simple, slightly different
`forecastML`

setup for the seatbelt forecasting problem from the previous example.

```
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)
```

- Let’s look at the content of our
`Python`

modeling file that we source()’d above. The`Python`

wrapper function inputs and returns for`py_model_function()`

and`py_prediction_function()`

are the same as their`R`

counterparts. Just be sure to expect and return`pandas`

`DataFrame`

s as conversion from`numpy`

arrays has not been tested.

```
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 and predict on historical validation data with the imported
`Python`

wrapper functions.

```
# 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))
```

- Forecast with the same imported
`Python`

wrapper functions. The final wrapper functions may eventually have fixed hyperparameters or complicated model ensembles based on repeated model training and investigation.

```
# 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))
```

- Refactor to incorporate
`tsibble`

time series datasets and principles.