Reference

All functions

agnosticgarchf()

ANY MODEL+GARCH(1, 1) forecasting

armagarchf()

ARMA(1, 1)-GARCH(1, 1) forecasting (with simulation)

basicf()

Basic forecasting (mean, median, random walk)

comb_GLMNET()

GLMNET Regression Forecast Combination

comb_OLS()

Ordinary Least Squares Forecast Combination

comb_Ridge()

Ridge Regression Forecast Combination

computeattention()

Compute global attention weights and context vectors for time series

conformalize()

Conformalize a forecasting function

contextridge2f()

Ridge Regression Forecasting with Attention-based Context Vectors

create_2level_hts()

Create simple 2-level hierarchical time series

createtrendseason()

Create trend and seasonality features for univariate time series

ctxthetaf()

Context-Aware Theta method forecast

direct_sampling()

Direct sampling

dynrmf()

Dynamic regression model

dynrmf_shap()

Compute exact Shapley values for an ahead::dynrmf model

eatf()

Combined ets-arima-theta forecasts

example_2level_forecast()

Example usage

fit_func()

Fit univariate time series using caret ML model (for use with dynrmf)

fitforecast()

Fit and forecast for benchmarking purposes

generate_synthetic_ts()

Generate synthetic time series via model-based residual bootstrap

genericforecast()

Generic Forecasting Function (Unified interface)

geterror()

Get error metrics

getreturns()

Calculate returns or log-returns for multivariate time series

getsimulations()

Obtain simulations (when relevant) from a selected time series

glmthetaf()

Generalized Linear Model Theta Forecast and not only

loessf()

Loess forecasting

loocvridge2f()

LOOCV for Ridge2 model

meboot()

Maximum Entropy Bootstrap for Time Series using Rcpp

ml_forecast()

Forecasting using Machine Leaning models

mlarchf()

Conformalized Forecasting using Machine Learning (and statistical) models with ARCH effects

mlf()

Conformalized Forecasting using Machine Leaning models

plot(<foreccomb_res>)

Plot results from forecast combination model

plot(<mtsforecast>)

Plot multivariate time series forecast or residuals

plot(<synthetic_ts>)

Plot method for synthetic_ts objects

plot_dynrmf_shap_waterfall()

Waterfall plot for a dynrmf_shap object

plot_hts_forecast()

Plot forecast results with simulation intervals

plot_simulations()

Plot simulation paths

predict(<foreccomb_res>)

Prediction function for Forecast Combinations

predict_func()

Predict univariate time series using caret ML model(for use with dynrmf)

print(<summary.synthetic_ts>)

Print method for summary.synthetic_ts objects

rfitdistr()

Simulate from parametric distribution

rgaussiandens()

Simulate Gaussian Kernel Density

ridge2f()

Ridge2 model

rmultivariate()

Simulate multivariate data

rsurrogate()

Simulate using surrogate data

sequential_conformal_hts()

Sequential split conformal prediction for hierarchical forecasting Returns simulations at both total and bottom levels

simple_forecast()

Simple ETS forecast function

splitts()

Partition a time series object

stackridge2f()

Stacked Doubly-Constrained RVFL for Multivariate Forecasting

summary(<foreccomb_res>) print(<foreccomb_res_summary>)

Summary of Forecast Combination

summary(<synthetic_ts>)

Summary method for synthetic_ts objects

topdown_forecast()

Top-down forecast using historical proportions

transformerreturnsf()

Transformer Forecasting for Financial Returns

varf()

Vector Autoregressive model (adapted from vars::VAR)