Package: TSstudio 0.1.7

Rami Krispin

TSstudio: Functions for Time Series Analysis and Forecasting

Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

Authors:Rami Krispin [aut, cre]

TSstudio_0.1.7.tar.gz
TSstudio_0.1.7.zip(r-4.5)TSstudio_0.1.7.zip(r-4.4)TSstudio_0.1.7.zip(r-4.3)
TSstudio_0.1.7.tgz(r-4.4-any)TSstudio_0.1.7.tgz(r-4.3-any)
TSstudio_0.1.7.tar.gz(r-4.5-noble)TSstudio_0.1.7.tar.gz(r-4.4-noble)
TSstudio_0.1.7.tgz(r-4.4-emscripten)TSstudio_0.1.7.tgz(r-4.3-emscripten)
TSstudio.pdf |TSstudio.html
TSstudio/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/ramikrispin/tsstudio/issues

Datasets:
  • Coffee_Prices - Coffee Prices: Robusta and Arabica
  • EURO_Brent - Crude Oil Prices: Brent - Europe
  • Michigan_CS - University of Michigan Consumer Survey, Index of Consumer Sentiment
  • USUnRate - US Monthly Civilian Unemployment Rate
  • USVSales - US Monthly Total Vehicle Sales
  • US_indicators - US Key Indicators - data frame format
  • USgas - US monthly natural gas consumption

On CRAN:

forecastingtime-seriestimeseriestsstudiovisualization

39 exports 421 stars 6.98 score 102 dependencies 2 mentions 584 scripts 4.6k downloads

Last updated 1 years agofrom:3a24a98a21. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 02 2024
R-4.5-winNOTESep 02 2024
R-4.5-linuxNOTESep 02 2024
R-4.4-winNOTESep 02 2024
R-4.4-macNOTESep 02 2024
R-4.3-winNOTESep 02 2024
R-4.3-macNOTESep 02 2024

Exports:add_horizonadd_inputadd_leveladd_methodsadd_train_methodadd_xregarima_diagbuild_modelccf_plotcheck_rescreate_modelforecast_simplot_errorplot_forecastplot_gridplot_modelremove_methodsres_histset_errortest_forecasttrain_modelts_corts_decomposets_gridts_heatmapts_infots_lagsts_mats_plotts_polarts_quantilets_reshapets_seasonalts_splitts_sumts_surfacets_to_prophetxts_to_tszoo_to_ts

Dependencies:anytimeaskpassbase64encBHbslibcachemclicodetoolscolorspacecpp11crosstalkcurldata.tabledigestdoParalleldplyrellipsisevaluatefansifarverfastmapfontawesomeforeachforecastforecastHybridfracdifffsgenericsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetshtshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelmtestlubridatemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetopensslpillarpkgconfigplotlyplyrpromisespurrrquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangrmarkdownsassscalesSparseMstringistringrsysthieftibbletidyrtidyselecttimechangetimeDatetinytextseriestsibbleTTRurcautf8vctrsviridisviridisLitewithrxfunxtsyamlzoo

Plotting Time Series Data

Rendered fromPlotting_Time_Series.Rmdusingknitr::rmarkdownon Sep 02 2024.

Last update: 2023-08-01
Started: 2018-09-01

Readme and manuals

Help Manual

Help pageTopics
Diagnostic Plots for ARIMA Modelsarima_diag
Time Series Cross Correlation Lags Visualizationccf_plot
Visualization of the Residuals of a Time Series Modelcheck_res
Coffee Prices: Robusta and ArabicaCoffee_Prices
A Functional Approach for Building the 'train_model' Componentsadd_horizon add_input add_level add_methods add_train_method add_xreg build_model create_model remove_methods set_error
Crude Oil Prices: Brent - EuropeEURO_Brent
Forecasting simulationforecast_sim
University of Michigan Consumer Survey, Index of Consumer SentimentMichigan_CS
Plot the Models Error Rates on the Testing Partitionsplot_error
Plotting Forecast Objectplot_forecast
Visualizing Grid Search Resultsplot_grid
Plot the Models Performance on the Testing Partitionsplot_model
Histogram Plot of the Residuals Valuesres_hist
Visualize of the Fitted and the Forecasted vs the Actual Valuestest_forecast
Train, Test, Evaluate, and Forecast Multiple Time Series Forecasting Modelstrain_model
An Interactive Visualization of the ACF and PACF Functionsts_cor
Visualization of the Decompose of a Time Series Objectts_decompose
Tuning Time Series Forecasting Models Parameters with Grid Searchts_grid
Heatmap Plot for Time Seriests_heatmap
Get the Time Series Informationts_info
Time Series Lag Visualizationts_lags
Moving Average Method for Time Series Datats_ma
Plotting Time Series Objectsts_plot
Polor Plot for Time Series Objectts_polar
Quantile Plot for Time Seriests_quantile
Transform Time Series Object to Data Frame Formatts_reshape
Seasonality Visualization of Time Series Objectts_seasonal
Split Time Series Object for Training and Testing Partitionsts_split
Summation of Multiple Time Series Objectsts_sum
3D Surface Plot for Time Seriests_surface
Transform Time Series Object to Prophet inputts_to_prophet
US Key Indicators - data frame formatUS_indicators
US monthly natural gas consumptionUSgas
US Monthly Civilian Unemployment RateUSUnRate
US Monthly Total Vehicle SalesUSVSales
Converting 'xts' object to 'ts' objectxts_to_ts
Converting 'zoo' object to 'ts' objectzoo_to_ts