Package: ahead 0.37.2

T. Moudiki

ahead: Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Authors:T. Moudiki

ahead_0.37.2.tar.gz
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ahead_0.37.2.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

7.65 score 23 stars 60 scripts 46 exports 47 dependencies

Last updated from:609788d77a. Checks:11 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING272
linux-devel-x86_64WARNING280
source / vignettesOK374
linux-release-arm64WARNING303
linux-release-x86_64WARNING275
macos-release-arm64WARNING172
macos-release-x86_64WARNING290
macos-oldrel-arm64WARNING155
macos-oldrel-x86_64WARNING361
windows-develWARNING244
windows-releaseWARNING239
windows-oldrelWARNING235
wasm-releaseOK208

Exports:agnosticgarchfarmagarchfbasicfcomb_GLMNETcomb_OLScomb_Ridgecomputeattentioncondvolfconformalizecontextridge2fcreatetrendseasonctxthetafdirect_samplingdynrmfdynrmf_sensidynrmf_shapeatfestimate_theta_slopefit_funcfitdistr_aheadfitforecastgenerate_synthetic_tsgenericforecastgeterrorgetreturnsgetsimulationsglmthetafloessfloocvridge2fmebootml_forecastmlarchfmlfplot_dynrmf_sensitivityplot_dynrmf_shap_waterfallpredict_funcremovenasrfitdistrrgaussiandensridgeridge2frmultivariatersurrogatesimulatorstackridge2fvarf

Dependencies:clicodetoolscolorspacecpp11curldoSNOWfarverforeachforecastfracdiffgenericsggplot2gluegtableisobanditeratorsjsonlitelabelinglatticelifecyclelmtestmagrittrMASSmiscnlmennetquadprogquantmodR6randtoolboxRColorBrewerRcppRcppArmadillorlangrngWELLS7scalessnowtimeDatetseriesTTRurcavctrsviridisLitewithrxtszoo

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

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

Last update: 2026-02-12
Started: 2025-03-07

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

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

Last update: 2025-06-21
Started: 2025-06-17

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

Last update: 2025-06-17
Started: 2025-02-12

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 for benchmarking purposes
Rendered fromfitforecastbench.Rmdusingknitr::rmarkdown

Last update: 2024-11-21
Started: 2024-03-25

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
Example usageexample_2level_forecast
Fit univariate time series using caret ML model (for use with 'dynrmf')fit_func
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 forecast results with simulation intervalsplot_hts_forecast
Plot simulation pathsplot_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
Simulate from parametric distributionrfitdistr
Simulate Gaussian Kernel Densityrgaussiandens
Ridge2 modelridge2f
Simulate multivariate datarmultivariate
Simulate using surrogate datarsurrogate
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