Package: timetk 2.9.1.9000

Matt Dancho

timetk: A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Authors:Matt Dancho [aut, cre], Davis Vaughan [aut]

timetk_2.9.1.9000.tar.gz
timetk_2.9.1.9000.zip(r-4.7)timetk_2.9.1.9000.zip(r-4.6)timetk_2.9.1.9000.zip(r-4.5)
timetk_2.9.1.9000.tgz(r-4.6-any)timetk_2.9.1.9000.tgz(r-4.5-any)
timetk_2.9.1.9000.tar.gz(r-4.7-any)timetk_2.9.1.9000.tar.gz(r-4.6-any)
timetk_2.9.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
timetk/json (API)

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

Bug tracker:https://github.com/business-science/timetk/issues

Pkgdown/docs site:https://business-science.github.io

Datasets:
  • bike_sharing_daily - Daily Bike Sharing Data
  • FANG - Stock prices for the "FANG" stocks.
  • m4_daily - Sample of 4 Daily Time Series Datasets from the M4 Competition
  • m4_hourly - Sample of 4 Hourly Time Series Datasets from the M4 Competition
  • m4_monthly - Sample of 4 Monthly Time Series Datasets from the M4 Competition
  • m4_quarterly - Sample of 4 Quarterly Time Series Datasets from the M4 Competition
  • m4_weekly - Sample of 4 Weekly Time Series Datasets from the M4 Competition
  • m4_yearly - Sample of 4 Yearly Time Series Datasets from the M4 Competition
  • taylor_30_min - Half-hourly electricity demand
  • walmart_sales_weekly - Sample Time Series Retail Data from the Walmart Recruiting Store Sales Forecasting Competition
  • wikipedia_traffic_daily - Sample Daily Time Series Data from the Web Traffic Forecasting (Wikipedia) Competition

On CRAN:

Conda:

coercioncoercion-functionsdata-miningdplyrforecastforecastingforecasting-modelsmachine-learningseries-decompositionseries-signaturetibbletidytidyquanttidyversetimetime-seriestimeseries

14.17 score 644 stars 17 packages 4.4k scripts 15k downloads 119 exports 127 dependencies

Last updated from:5a06bd8b5d. Checks:9 OK. Indexed: yes.

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Dependencies:anytimeaskpassbase64encBHbitbit64bslibcachemclassclicliprclockcodetoolscolorspacecpp11crayoncrosstalkcurldata.tablediagramdigestdplyrevaluatefarverfastmapfontawesomeforcatsforecastfracdifffsfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathighrhmshtmltoolshtmlwidgetshttripredisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlmtestlubridatemagrittrMASSMatrixmemoisemimenlmennetnumDerivopensslotelpadrparallellypillarpkgconfigplotlyprettyunitsprodlimprogressprogressrpromisespurrrquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppRollreadrrecipesrlangrmarkdownrpartrsampleS7sassscalesshapeslidersparsevctrsSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextseriestsfeaturesTTRtzdburcautf8vctrsviridisLitevroomwarpwithrxfunxtsyamlzoo

Anomaly Detection
Data | Visualization | Anomalize: breakdown, identify, and clean in 1 easy step | Anomaly Visualization 1: Seasonal Decomposition Plot | Anomaly Visualization 2: Anomaly Detection Plot | Anomaly Visualization 3: Anomalies Cleaned Plot | Learning More
Rendered fromTK08_Automatic_Anomaly_Detection.Rmdusingknitr::rmarkdown

Last update: 2023-12-08
Started: 2020-04-23

Calendar Features
Introduction | Prerequisites | Data | Time Series Index | Time Series Signature | Get Functions - Turning an Index into Information | Augment Functions (Adding Many Features to a Data Frame) | Time Series Summary | Learning More
Rendered fromTK01_Working_With_Time_Series_Index.Rmdusingknitr::rmarkdown

Last update: 2023-10-03
Started: 2017-04-29

Frequency and Trend Selection
Prerequisites | Data | Applications | Automatic Frequency & Trend Selection | Specifying a Frequency or Trend | Frequency | Trend | Time Scale Template | Learning More
Rendered fromTK06_Automatic_Frequency_And_Trend_Selection.Rmdusingknitr::rmarkdown

Last update: 2023-10-03
Started: 2020-04-14

Time Series Class Conversion
Introduction | Prerequisites | Data | Case Study: Conversion issues with ts() | Problems | Solution | Advantages of conversion with tk_tbl() | Conversion Methods | From tbl | to xts | to zoo | to zooreg | to ts | To tbl | From xts | From zoo | From zooreg | From ts | Testing if an object has a timetk index | tk_ts() | Testing other data types | Working with zoo::yearmon and zoo::yearqtr index | Learning More
Rendered fromTK00_Time_Series_Coercion.Rmdusingknitr::rmarkdown

Last update: 2023-10-03
Started: 2017-04-19

Time Series Machine Learning
Introduction | Prerequisites | Data | Train / Test | Modeling | Recipe Preprocessing Specification | Model Specification | Workflow | Training | Hyperparameter Tuning | Forecasting with Modeltime | Modeltime Table | Calibration | Forecast (Testing Set) | Accuracy (Testing Set) | Refit and Forecast Forward | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting
Rendered fromTK03_Forecasting_Using_Time_Series_Signature.Rmdusingknitr::rmarkdown

Last update: 2023-10-03
Started: 2017-05-13

Visualizing Time Series
Libraries | Plotting Time Series | Plotting Groups | Visualizing Transformations & Sub-Groups | Static ggplot2 Visualizations & Customizations | Box Plots (Time Series) | Regression Plots (Time Series) | Summary | Take the High-Performance Forecasting Course | Time Series is Changing | How to Learn High-Performance Time Series Forecasting
Rendered fromTK04_Plotting_Time_Series.Rmdusingknitr::rmarkdown

Last update: 2023-10-03
Started: 2020-04-13

Time Series Data Wrangling
Libraries | Data | Summarize by Time | Period Summarization | Period Smoothing | Filter By Time | Time Range Filtering | Padding Data | Fill in Gaps | Low to High Frequency | Sliding (Rolling) Calculations | Rolling Mean | Rolling Regression | Learning More
Rendered fromTK07_Time_Series_Data_Wrangling.Rmdusingknitr::rmarkdown

Last update: 2023-10-02
Started: 2020-04-17

Intelligent Date & Time Sequences
Prerequisites | Making a Time Series Sequence | Future Time Series Sequence | Weekends & Holidays | Learning More
Rendered fromTK02_Time_Series_Date_Sequences.Rmdusingknitr::rmarkdown

Last update: 2023-03-29
Started: 2020-04-14

Time Series Clustering
Libraries | Data | TS Features | Clustering with K-Means | Visualize the Cluster Assignments | Learning More
Rendered fromTK09_Clustering.Rmdusingknitr::rmarkdown

Last update: 2023-03-20
Started: 2021-11-16

Plotting Seasonality and Correlation
Libraries | Correlation Plots | Grouped ACF Diagnostics | Grouped CCF Plots | Seasonality | Seasonal Visualizations | Grouped Seasonal Visualizations | STL Diagnostics | Learning More
Rendered fromTK05_Plotting_Seasonality_and_Correlation.Rmdusingknitr::rmarkdown

Last update: 2023-03-20
Started: 2020-04-14

Readme and manuals

Help Manual

Help pageTopics
timetk: Time Series Analysis in the Tidyversetimetk-package timetk
Automatic group-wise Anomaly Detectionanomalize
Between (For Time Series): Range detection for date or date-time sequencesbetween_time
Daily Bike Sharing Databike_sharing_daily
Box Cox Transformationauto_lambda box_cox_inv_vec box_cox_vec
Convert the Period to a Lower Periodicity (e.g. Go from Daily to Monthly)condense_period
Differencing Transformationdiff_inv_vec diff_vec
Stock prices for the "FANG" stocks.FANG
Filter (for Time-Series Data)filter_by_time
Apply filtering expressions inside periods (windows)filter_period
Fourier Seriesfourier_vec
Make future time series from existingfuture_frame
Check if an object is a date classis_date_class
Lag Transformationlag_vec lead_vec
Log-Interval Transformation for Constrained Interval Forecastinglog_interval_inv_vec log_interval_vec
Sample of 4 Daily Time Series Datasets from the M4 Competitionm4_daily
Sample of 4 Hourly Time Series Datasets from the M4 Competitionm4_hourly
Sample of 4 Monthly Time Series Datasets from the M4 Competitionm4_monthly
Sample of 4 Quarterly Time Series Datasets from the M4 Competitionm4_quarterly
Sample of 4 Weekly Time Series Datasets from the M4 Competitionm4_weekly
Sample of 4 Yearly Time Series Datasets from the M4 Competitionm4_yearly
Mutate (for Time Series Data)mutate_by_time
Normalize to Range (0, 1)normalize_inv_vec normalize_vec
Insert time series rows with regularly spaced timestampspad_by_time
Fast, flexible date and datetime parsingparse_date2 parse_datetime2
Visualize the ACF, PACF, and CCFs for One or More Time Seriesplot_acf_diagnostics
Visualize Anomalies for One or More Time Seriesplot_anomalies plot_anomalies_cleaned plot_anomalies_decomp
Visualize Anomalies for One or More Time Seriesplot_anomaly_diagnostics
Visualize Multiple Seasonality Features for One or More Time Seriesplot_seasonal_diagnostics
Visualize STL Decomposition Features for One or More Time Seriesplot_stl_diagnostics
Interactive Plotting for One or More Time Seriesplot_time_series
Interactive Time Series Box Plotsplot_time_series_boxplot
Visualize a Time Series Resample Planplot_time_series_cv_plan
Visualize a Time Series Linear Regression Formulaplot_time_series_regression
Get and modify the Time Scale Templateget_tk_time_scale_template set_tk_time_scale_template tk_time_scale_template
Apply slice inside periods (windows)slice_period
Create a rolling (sliding) version of any functionslidify
Rolling Window Transformationslidify_vec
Smoothing Transformation using Loesssmooth_vec
Standardize to Mean 0, Standard Deviation 1 (Center & Scale)standardize_inv_vec standardize_vec
Box-Cox Transformation using Forecast Methodsstep_box_cox tidy.step_box_cox
Create a differenced predictorstep_diff tidy.step_diff
Fourier Features for Modeling Seasonalitystep_fourier tidy.step_fourier
Holiday Feature (Signature) Generatorstep_holiday_signature tidy.step_holiday_signature
Log Interval Transformation for Constrained Interval Forecastingstep_log_interval tidy.step_log_interval
Slidify Rolling Window Transformationstep_slidify tidy.step_slidify
Slidify Rolling Window Transformation (Augmented Version)step_slidify_augment tidy.step_slidify_augment
Smoothing Transformation using Loessstep_smooth tidy.step_smooth
Time Series Feature (Signature) Generatorstep_timeseries_signature tidy.step_timeseries_signature
Clean Outliers and Missing Data for Time Seriesstep_ts_clean tidy.step_ts_clean
Missing Data Imputation for Time Seriesstep_ts_impute tidy.step_ts_impute
Pad: Add rows to fill gaps and go from low to high frequencystep_ts_pad tidy.step_ts_pad
Summarise (for Time Series Data)summarise_by_time summarize_by_time
Half-hourly electricity demandtaylor_30_min
Add / Subtract (For Time Series)%+time% %-time% add_time subtract_time time_arithmetic
Time Series Cross Validationtime_series_cv
Simple Training/Test Set Splitting for Time Seriestime_series_split
Group-wise ACF, PACF, and CCF Data Preparationtk_acf_diagnostics
Automatic group-wise Anomaly Detection by STL Decompositiontk_anomaly_diagnostics
Add many differenced columns to the datatk_augment_differences
Add many fourier series to the datatk_augment_fourier
Add many holiday features to the datatk_augment_holiday tk_augment_holiday_signature
Add many lags to the datatk_augment_lags tk_augment_leads
Add many rolling window calculations to the datatk_augment_slidify
Add many time series features to the datatk_augment_timeseries tk_augment_timeseries_signature
Automatic frequency and trend calculation from a time series indextk_get_frequency tk_get_trend
Get holiday features from a time-series indextk_get_holiday tk_get_holidays_by_year tk_get_holiday_signature
Get date features from a time-series indextk_get_timeseries tk_get_timeseries_signature tk_get_timeseries_summary
Get the timeseries unit frequency for the primary time scalestk_get_timeseries_unit_frequency
Get date or datetime variables (column names)tk_get_timeseries_variables
Extract an index of date or datetime from time series objects, models, forecastshas_timetk_idx tk_index
Make future time series from existingtk_make_future_timeseries
Make daily Holiday and Weekend date sequencestk_make_holiday_sequence tk_make_weekday_sequence tk_make_weekend_sequence
Intelligent date and date-time sequence creationtk_make_timeseries
Group-wise Seasonality Data Preparationtk_seasonal_diagnostics
Group-wise STL Decomposition (Season, Trend, Remainder)tk_stl_diagnostics
Group-wise Time Series Summarytk_summary_diagnostics
Coerce time-series objects to tibble.tk_tbl
Time Series Resample Plan Data Preparationtk_time_series_cv_plan
Coerce time series objects and tibbles with date/date-time columns to ts.tk_ts tk_ts_
Time series feature matrix (Tidy)tk_tsfeatures
Coerce time series objects and tibbles with date/date-time columns to xts.tk_xts tk_xts_
Coerce time series objects and tibbles with date/date-time columns to xts.tk_zoo tk_zoo_
Coerce time series objects and tibbles with date/date-time columns to ts.tk_zooreg tk_zooreg_
Replace Outliers & Missing Values in a Time Seriests_clean_vec
Missing Value Imputation for Time Seriests_impute_vec
Sample Time Series Retail Data from the Walmart Recruiting Store Sales Forecasting Competitionwalmart_sales_weekly
Sample Daily Time Series Data from the Web Traffic Forecasting (Wikipedia) Competitionwikipedia_traffic_daily