Package: ahead 0.38.8

T. Moudiki

ahead: Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Authors:T. Moudiki

ahead_0.38.8.tar.gz
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ahead_0.38.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ahead/json (API)

# Install 'ahead' in R:
install.packages('ahead', repos = c('https://techtonique.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/techtonique/ahead/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

forecastingmachine-learningpredictive-modelingstatistical-learningtime-seriestime-series-forecastinguncertainty-quantificationcpp

7.54 score 23 stars 60 scripts 47 exports 13 dependencies

Last updated from:dc215f1fb3. Checks:13 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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source / vignettesOK331
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macos-release-arm64WARNING166
macos-release-x86_64WARNING356
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windows-devel-arm64WARNING352
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windows-release-x86_64WARNING226
windows-oldrel-x86_64WARNING207
wasm-releaseOK197

Exports:agnosticgarchfarmagarchfbasicfcomb_GLMNETcomb_OLScomb_Ridgecomputeattentioncondvolfconformalizecontextridge2fcreatetrendseasonctxthetafdirect_samplingdynrmfdynrmf_sensidynrmf_shapeatfestimate_theta_slopefit_funcfitdistr_aheadfitforecastgenerate_synthetic_tsgenericforecastgeterrorgetreturnsgetsimulationsglmthetafloessfloocvridge2fmebootml_forecastmlarchfmlfplot_dynrmf_sensitivityplot_dynrmf_shap_waterfallpredict_funcremovenasrfitdistrrgaussiandensridgeridge2frmultivariatersurrogatesimulatorsplittsstackridge2fvarf

Dependencies:codetoolscurlforeachiteratorsjsonlitelatticequadprogquantmodRcpptseriesTTRxtszoo

Fit and forecast for benchmarking purposes
Rendered fromfitforecastbench.Rmdusingknitr::rmarkdown

Last update: 2026-09-05
Started: 2024-03-25

Conformal Prediction using Ridge2
Univariate | AirPassengers | USAccDeaths | Multivariate
Rendered fromahead-ridge2-conformal.Rmdusingknitr::rmarkdown

Last update: 2026-08-29
Started: 2025-03-07

Conformalized Forecasting using Machine Leaning models
Rendered fromconformal-ml.Rmdusingknitr::rmarkdown

Last update: 2026-08-29
Started: 2025-02-12

Conformalized Forecasting using Machine Leaning models 2
Rendered fromconformal-ml2.Rmdusingknitr::rmarkdown

Last update: 2026-08-29
Started: 2025-06-17

Generalized Linear Model Theta Forecast for any model
Rendered fromglmthetaf-anymodel.Rmdusingknitr::rmarkdown

Last update: 2026-05-08
Started: 2025-11-10

Prediction intervals for multivariate time series (simulation-based)
0 - Install ahead | 1 - Prediction intervals based on Gaussian distribution (meh, but quick) | 1 - 1 With default parameters | 1 - 2 With external regressors | 1 - 3 With external regressors and K-Means clustering | 1 - 4 With K-Means clustering | 2 - Prediction intervals based on independent bootstrap | 2 - 1 With default parameters | 2 - 2 With external regressors | 2 - 3 With external regressors and K-Means clustering | 2 - 4 With K-Means clustering | 3 - Prediction intervals based on block bootstrap | 3 - 1 With default parameters | 3 - 2 With external regressors | 3 - 3 With external regressors and K-Means clustering | 3 - 4 With K-Means clustering | 3 - 5 Using the median instead of the mean in bootstrap aggregation | 4 - Prediction intervals based on moving block bootstrap | 4 - 1 With default parameters | 4 - 2 With external regressors | 4 - 3 With external regressors and K-Means clustering | 4 - 4 With K-Means clustering | 4 - 5 Using the median instead of the mean in bootstrap aggregation | 5 - Prediction intervals based on R-Vine copula simulation
Rendered fromahead-ridge2-prediction-intervals.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2023-08-12

Introduction to R package ahead
Univariate time series | Multivariate time series
Rendered fromahead-vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2021-10-14

Plotting functions
Install ahead | Forecasting and plot predictions
Rendered fromahead-plotting-functions.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2023-08-23

Prediction intervals for Loess forecasting (simulation-based)
Install ahead | ahead::loessf on Nile dataset
Rendered fromahead-loess-forecasting.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2023-08-12

Risk-neutralize simulations
0 - Install ahead | 1 - Get and transform data | 2 - Risk-neutralize simulations | 2 - 1 Yield to maturities (fake risk-free rates) | 2 - 2 Risk-neutralized simulations | 3 - Visualization
Rendered fromahead-neutralize.Rmdusingknitr::rmarkdown

Last update: 2026-02-12
Started: 2023-09-03

Synthetic simulation
Rendered fromsynthsimulation.Rmdusingknitr::rmarkdown

Last update: 2025-11-13
Started: 2025-11-09

Context-aware Theta
Rendered fromctx-theta.Rmdusingknitr::rmarkdown

Last update: 2025-11-12
Started: 2025-11-12

Generalized Linear Model Theta Forecast with attention Pt.3
Rendered fromglmthetaf-attention3.Rmdusingknitr::rmarkdown

Last update: 2025-11-10
Started: 2025-11-09

Generalized Linear Model Theta Forecast with attention Pt.2
Rendered fromglmthetaf-attention2.Rmdusingknitr::rmarkdown

Last update: 2025-11-09
Started: 2025-11-09

Stacking ridge2f
Rendered fromstacking-ridge2f.Rmdusingknitr::rmarkdown

Last update: 2025-10-13
Started: 2025-10-13

Max Entropy Bootstrap
Rendered frommeboost.Rmdusingknitr::rmarkdown

Last update: 2025-10-02
Started: 2025-10-02

Any model + GARCH(1, 1)
Rendered fromanymodelplusgarch.Rmdusingknitr::rmarkdown

Last update: 2025-09-24
Started: 2025-09-23

Beyond GARCH
Introduction | Basic Usage | Different Machine Learning Methods | Using caret Models | Customizing Mean and Residual Models
Rendered frommlarch.Rmdusingknitr::rmarkdown

Last update: 2025-08-01
Started: 2025-06-03

Beyond GARCH 2
Rendered frommlarch2.Rmdusingknitr::rmarkdown

Last update: 2025-06-21
Started: 2025-06-21

Generalized Linear Model Theta Forecast with attention
USAccDeaths (method="adj") | AirPassengers (method="adj") | USAccDeaths (method='adj') | AirPassengers (method='adj')
Rendered fromglmthetaf-attention.Rmdusingknitr::rmarkdown

Last update: 2025-06-03
Started: 2025-03-12

Generalized Linear Model Theta Forecast with attention - conformal uncertainty
USAccDeaths (method='adj') | AirPassengers (method='adj')
Rendered fromglmthetaf-attention-conformal.Rmdusingknitr::rmarkdown

Last update: 2025-06-03
Started: 2025-03-12

Ridge Theta Forecast with attention - conformal uncertainty
USAccDeaths | AirPassengers
Rendered fromglmthetaf-ridge-attention.Rmdusingknitr::rmarkdown

Last update: 2025-06-03
Started: 2025-04-11

Generalized Linear Model Theta Forecast
USAccDeaths | AirPassengers
Rendered fromglmthetaf.Rmdusingknitr::rmarkdown

Last update: 2025-04-07
Started: 2025-03-10

comb OLS AirPassengers
1 - Comb OLS | 2 - Comb Ridge
Rendered fromcomb_OLS2.Rmdusingknitr::rmarkdown

Last update: 2024-11-21
Started: 2024-10-24

comb OLS Electricity
1 - Comb OLS | 2 - Comb Ridge
Rendered fromcomb_OLS.Rmdusingknitr::rmarkdown

Last update: 2024-11-21
Started: 2024-10-24

Generic forecasting and conformal prediction
0 - Packages | 1 - Generic forecaster | 2 - Conformal prediction
Rendered fromgeneric-conformal.Rmdusingknitr::rmarkdown

Last update: 2024-11-21
Started: 2024-11-21

Fit and forecast using caret+dynrmf
Rendered fromcaretfitpredict.Rmdusingknitr::rmarkdown

Last update: 2024-10-24
Started: 2024-10-24

Univariate forecasting with ridge2f in ahead
Rendered fromahead-univariate-season.Rmdusingknitr::rmarkdown

Last update: 2024-03-10
Started: 2024-01-20

Readme and manuals

Help Manual

Help pageTopics
ANY MODEL+GARCH(1, 1) forecastingagnosticgarchf
ARMA(1, 1)-GARCH(1, 1) forecasting (with simulation)armagarchf
Basic forecasting (mean, median, random walk)basicf
GLMNET Regression Forecast Combinationcomb_GLMNET
Ordinary Least Squares Forecast Combinationcomb_OLS
Ridge Regression Forecast Combinationcomb_Ridge
Compute global attention weights and context vectors for time seriescomputeattention
Model-agnostic statistical probabilistic forecasting with conditional volatilitycondvolf
Conformalize a forecasting functionconformalize
Ridge Regression Forecasting with Attention-based Context Vectorscontextridge2f
Create simple 2-level hierarchical time seriescreate_2level_hts
Create trend and seasonality features for univariate time seriescreatetrendseason
Context-Aware Theta method forecastctxthetaf
Direct samplingdirect_sampling
Dynamic regression modeldynrmf
Compute First-Order Sensitivity Effects for Dynamic Regression Forecastsdynrmf_sensi
Compute exact Shapley values for an ahead::dynrmf modeldynrmf_shap
Combined ets-arima-theta forecastseatf
Estimate local slope of a fitted model's response surface with respect to timeestimate_theta_slope
Example usageexample_2level_forecast
Fit univariate time series using caret ML model (for use with 'dynrmf')fit_func
Fit a Univariate Distribution to Data via Maximum Likelihoodfitdistr_ahead
Fit and forecast for benchmarking purposesfitforecast
Generate synthetic time series via model-based residual bootstrapgenerate_synthetic_ts
Generic Forecasting Function (Unified interface)genericforecast
Get error metricsgeterror
Calculate returns or log-returns for multivariate time seriesgetreturns
Obtain simulations (when relevant) from a selected time seriesgetsimulations
Generalized Linear Model Theta Forecast and not onlyglmthetaf
Loess forecastingloessf
LOOCV for Ridge2 modelloocvridge2f
Maximum Entropy Bootstrap for Time Series using Rcppmeboot
Forecasting using Machine Leaning modelsml_forecast
Conformalized Forecasting using Machine Learning (and statistical) models with ARCH effectsmlarchf
Conformalized Forecasting using Machine Leaning modelsmlf
Plot First-Order Sensitivity Effectsplot_dynrmf_sensitivity
Waterfall plot for a dynrmf_shap objectplot_dynrmf_shap_waterfall
Plot hierarchical time series forecastplot_hts_forecast
Plot simulation paths for a hierarchical time series forecastplot_simulations
Plot results from forecast combination modelplot.foreccomb_res
Plot multivariate time series forecast or residualsplot.mtsforecast
Plot method for synthetic_ts objectsplot.synthetic_ts
Predict univariate time series using caret ML model(for use with 'dynrmf')predict_func
Prediction function for Forecast Combinationspredict.foreccomb_res
Print method for summary.synthetic_ts objectsprint.summary.synthetic_ts
Remove Missing Values from a Time Series via Linear Interpolationremovenas
Random generation from a fitted distributionrfitdistr
Simulate Gaussian Kernel Densityrgaussiandens
Fit Ridge Regression Across a Grid of Penalty Valuesridge
Ridge2 modelridge2f
Simulate multivariate datarmultivariate
Random generation of surrogate seriesrsurrogate
Sequential split conformal prediction for hierarchical forecasting Returns simulations at both total and bottom levelssequential_conformal_hts
Simple ETS forecast functionsimple_forecast
simulate from a forecasting functionsimulator
Partition a time series objectsplitts
Stacked Doubly-Constrained RVFL for Multivariate Forecastingstackridge2f
Summary of Forecast Combinationprint.foreccomb_res_summary summary.foreccomb_res
Summary method for synthetic_ts objectssummary.synthetic_ts
Top-down forecast using historical proportionstopdown_forecast
Vector Autoregressive model (adapted from vars::VAR)varf