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 aimed at
helping the user quickly assess the (a) accuracy, (b) stability, and (c) generalizability of 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 predictors and
- nested cross-validation with (a) user-specified standard cross-validation in the inner loop and (b) block-contiguous validation datasets in the outer loop
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."
devtools::install_github("nredell/forecastML")
library(forecastML)Detailed forecastML overview vignette.
# 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 predictor.
horizons <- 1:12
lookback <- 1:15
#------------------------------------------------------------------------------
# Create a dataset of lagged predictors for modeling.
data_train <- forecastML::create_lagged_df(data_seatbelts, type = "train",
outcome_cols = 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
# The model takes in a data.frame with a target and predictors with exactly the same format as
# in create_lagged_df(). 'outcome_cols' is the column index of the target. The
# model returns a model object suitable for a predict-type function.
library(glmnet)
model_function <- function(data, outcome_cols = 1) {
x <- data[, -(outcome_cols), drop = FALSE]
y <- data[, outcome_cols, drop = FALSE]
x <- as.matrix(x, ncol = ncol(x))
y <- as.matrix(y, ncol = ncol(y))
model <- glmnet::cv.glmnet(x, y)
return(model)
}
#------------------------------------------------------------------------------
# Train a model across forecast horizons and validation datasets.
model_results <- forecastML::train_model(data_train, windows,
model_function, model_name = "LASSO")
#------------------------------------------------------------------------------
# User-defined prediction function - LASSO
# The predict() wrapper takes two positional arguments. First,
# the returned model from the user-defined modeling function (model_function() above).
# Second, a data.frame of predictors--lagged predictors will be created automatically
# using create_lagged_df().
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"))
return(data_pred)
}
# Predict on the validation datasets.
data_valid <- predict(model_results, prediction_function = list(prediction_function))
#------------------------------------------------------------------------------
# Plot forecasts for each validation dataset.
plot(data_valid, horizons = c(1, 6, 12))
#------------------------------------------------------------------------------
# Forecast.
data_forecast <- forecastML::create_lagged_df(data_seatbelts, type = "forecast",
outcome_cols = 1,
lookback = lookback, horizons = horizons)
data_forecasts <- predict(model_results, prediction_function = list(prediction_function),
data_forecast = data_forecast)
plot(data_forecasts, data[-(1:150), ], horizons = c(1, 6, 12))
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