Package: tscv 1.0.1

tscv: Functions and Utilities for Tidy Time Series Forecasting and Time Series Cross-Validation

Functions and tools for tidy time series analysis and forecasting as well as time series cross-validation. This is mainly a set of wrapper and helper functions as well as some extensions for the packages 'tsibble', 'fable', and 'fabletools'.

Authors:Alexander Häußer [aut, cre, cph]

tscv_1.0.1.tar.gz
tscv_1.0.1.zip(r-4.7-any)tscv_1.0.1.zip(r-4.6-any)tscv_1.0.1.zip(r-4.5-any)
tscv_1.0.1.tgz(r-4.6-any)tscv_1.0.1.tgz(r-4.5-any)
tscv_1.0.1.tar.gz(r-4.7-any)tscv_1.0.1.tar.gz(r-4.6-any)
tscv_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tscv/json (API)

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

Bug tracker:https://github.com/ahaeusser/tscv/issues

Pkgdown/docs site:https://ahaeusser.github.io

Datasets:

On CRAN:

Conda:

cross-validationexploratory-data-analysisfablefabletoolsforecast-accuracyforecastingggplot2time-seriestscvtsibblevisualization

6.07 score 9 stars 11 scripts 314 downloads 48 exports 75 dependencies

Last updated from:22b867c774. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK239
source / vignettesOK338
linux-releaseOK241
macos-releaseOK204
macos-oldrelOK171
windows-develOK190
windows-releaseOK186
windows-oldrelOK194
wasm-releaseOK147

Exports:acf_veccheck_dataDSHWestimate_acfestimate_kurtosisestimate_modeestimate_pacfestimate_skewnessforecast_naive2interpolate_missingmae_vecmake_accuracymake_errorsmake_futuremake_splitmake_tsibblemape_vecme_vecMEDIANmpe_vecmse_vecNAIVE2pacf_vecplot_barplot_densityplot_histogramplot_lineplot_pointplot_qqrmse_vecscale_color_tscvscale_fill_tscvslice_testslice_trainsmape_vecSMEANSMEDIANsmooth_outlierSNAIVE2split_indexsummarise_datasummarise_splitsummarise_statsTBATStest_seasonalitytheme_tscvtscv_colstscv_pal

Dependencies:anytimeBHbitopscaToolsclicodetoolscolorspacecpp11DEoptimRdigestdistributionaldoParalleldplyrfabletoolsfarverforeachforecastfracdiffgenericsggdistggplot2gluegtableisobanditeratorsjaneaustenrlabelinglatticelifecyclelmtestlubridatemagrittrMASSMatrixnlmennetnumDerivopdisDownsamplingpbmcapplypillarpkgconfigpracmaprogressrpurrrqqconfqqplotrquadprogR6RColorBrewerRcppRcppArmadillorlangrobustbaseS7scalessliderSnowballCstringistringrtibbletidyrtidyselecttidytexttimechangetimeDatetokenizerstsibbletwosamplesurcautf8vctrsviridisLitewarpwithrzoo

Expanding window approach
Installation | Example | Data preparation | Split data into training and testing | Training and forecasting | Visualize rolling forecasts | Forecast accuracy | Forecast accuracy by forecast horizon | Forecast accuracy by split | Summary
Rendered fromvignette_01_monthly_expanding.Rmdusingknitr::rmarkdown

Last update: 2026-05-15
Started: 2026-04-27

Fixed window approach
Installation | Example | Data preparation | Split data into training and testing | Training and forecasting | Evaluation of forecast accuracy | Forecast accuracy by forecast horizon | Forecast accuracy by split | Summary
Rendered fromvignette_02_hourly_fixed.Rmdusingknitr::rmarkdown

Last update: 2026-05-15
Started: 2026-04-27

Visualization of time series data
Installation | Example | Data preparation | Line charts | Bar charts | Distributions | Histograms | Density | QQ-Plot | Summary
Rendered fromvignette_03_data_visualization.Rmdusingknitr::rmarkdown

Last update: 2026-05-15
Started: 2021-04-23

Readme and manuals

Help Manual

Help pageTopics
Estimate autocorrelations of a numeric vectoracf_vec
Check and prepare tsibble datacheck_data
Double Seasonal Holt-Winters modelDSHW
Hourly electricity load (actual values and forecasts)elec_load
Hourly day-ahead electricity spot priceselec_price
Estimate autocorrelations by time seriesestimate_acf
Estimate kurtosisestimate_kurtosis
Estimate the mode of a distributionestimate_mode
Estimate partial autocorrelations by time seriesestimate_pacf
Estimate skewnessestimate_skewness
Extract fitted values from a DSHW modelfitted.DSHW
Extract fitted values from a median modelfitted.MEDIAN
Extract fitted values from a Naive2 modelfitted.NAIVE2
Extract fitted values from a seasonal mean modelfitted.SMEAN
Extract fitted values from a seasonal median modelfitted.SMEDIAN
Extract fitted values from a SNAIVE2 modelfitted.SNAIVE2
Extract fitted values from a TBATS modelfitted.TBATS
Forecast a time series using the Naive2 methodforecast_naive2
Forecast a DSHW modelforecast.DSHW
Forecast a median modelforecast.MEDIAN
Forecast a Naive2 modelforecast.NAIVE2
Forecast a seasonal mean modelforecast.SMEAN
Forecast a seasonal median modelforecast.SMEDIAN
Forecast a SNAIVE2 modelforecast.SNAIVE2
Forecast a TBATS modelforecast.TBATS
Interpolate missing valuesinterpolate_missing
Monthly time series data from the M4 CompetitionM4_monthly_data
Quarterly time series data from the M4 CompetitionM4_quarterly_data
Calculate the mean absolute errormae_vec
Estimate point forecast accuracymake_accuracy
Calculate forecast errors and percentage errorsmake_errors
Convert forecasts to a future framemake_future
Create train-test splits for time series cross-validationmake_split
Convert data to a tsibblemake_tsibble
Calculate the mean absolute percentage errormape_vec
Calculate the mean errorme_vec
Median modelMEDIAN
Summarize a DSHW modelmodel_sum.DSHW
Summarize a median modelmodel_sum.MEDIAN
Summarize a Naive2 modelmodel_sum.NAIVE2
Summarize a seasonal mean modelmodel_sum.SMEAN
Summarize a seasonal median modelmodel_sum.SMEDIAN
Summarize a SNAIVE2 modelmodel_sum.SNAIVE2
Summarize a TBATS modelmodel_sum.TBATS
Calculate the mean percentage errormpe_vec
Calculate the mean squared errormse_vec
Naive2 modelNAIVE2
Estimate partial autocorrelations of a numeric vectorpacf_vec
Plot data as a bar chartplot_bar
Plot a kernel density estimateplot_density
Plot data as a histogramplot_histogram
Plot data as a line chartplot_line
Plot data as a scatterplotplot_point
Create a quantile-quantile plotplot_qq
Extract residuals from a DSHW modelresiduals.DSHW
Extract residuals from a median modelresiduals.MEDIAN
Extract residuals from a Naive2 modelresiduals.NAIVE2
Extract residuals from a seasonal mean modelresiduals.SMEAN
Extract residuals from a seasonal median modelresiduals.SMEDIAN
Extract residuals from a SNAIVE2 modelresiduals.SNAIVE2
Extract residuals from a TBATS modelresiduals.TBATS
Calculate the root mean squared errorrmse_vec
Create a tscv color scalescale_color_tscv
Create a tscv fill scalescale_fill_tscv
Slice test data from a split frameslice_test
Slice training data from a split frameslice_train
Calculate the symmetric mean absolute percentage errorsmape_vec
Seasonal mean modelSMEAN
Seasonal median modelSMEDIAN
Identify and replace outlierssmooth_outlier
Seasonal naive model with weekday-specific lagsSNAIVE2
Create indices for train and test splitssplit_index
Summarise time series datasummarise_data
Summarise train-test splitssummarise_split
Summarise distributional statistics by time seriessummarise_stats
TBATS modelTBATS
Test a time series for seasonalitytest_seasonality
Custom ggplot2 theme for tscvtheme_tscv
Extract tscv colorstscv_cols
Create a tscv color palettetscv_pal