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-4.7)TSstudio_0.1.7.zip(r-4.6)TSstudio_0.1.7.zip(r-4.5)
TSstudio_0.1.7.tgz(r-4.6-any)TSstudio_0.1.7.tgz(r-4.5-any)
TSstudio_0.1.7.tar.gz(r-4.7-any)TSstudio_0.1.7.tar.gz(r-4.6-any)
TSstudio_0.1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TSstudio/json (API)

# Install 'TSstudio' in R:

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

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
US_indicators - US Key Indicators - data frame format
USgas - US monthly natural gas consumption
USUnRate - US Monthly Civilian Unemployment Rate
USVSales - US Monthly Total Vehicle Sales

On CRAN:

forecasting time-series timeseries tsstudio visualization

8.71 score 426 stars 750 scripts 1.6k downloads 2 mentions 39 exports 95 dependencies

Last updated from:3a24a98a21. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	192
source / vignettes	OK	260
linux-release-x86_64	NOTE	171
macos-release-arm64	NOTE	143
macos-oldrel-arm64	NOTE	144
windows-devel	NOTE	123
windows-release	NOTE	123
windows-oldrel	NOTE	114
wasm-release	OK	191

Exports:add_horizon add_input add_level add_methods add_train_method add_xreg arima_diag build_model ccf_plot check_res create_model forecast_sim plot_error plot_forecast plot_grid plot_model remove_methods res_hist set_error test_forecast train_model ts_cor ts_decompose ts_grid ts_heatmap ts_info ts_lags ts_ma ts_plot ts_polar ts_quantile ts_reshape ts_seasonal ts_split ts_sum ts_surface ts_to_prophet xts_to_ts zoo_to_ts

Dependencies:anytime askpass base64enc BH bslib cachem cli codetools colorspace cpp11 crosstalk curl data.table digest doParallel dplyr evaluate farver fastmap fontawesome foreach forecast forecastHybrid fracdiff fs generics ggplot2 glue gridExtra gtable highr htmltools htmlwidgets hts httr isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle lmtest lubridate magrittr Matrix memoise mime nlme nnet openssl otel pillar pkgconfig plotly plyr promises purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang rmarkdown S7 sass scales SparseM stringi stringr sys thief tibble tidyr tidyselect timechange timeDate tinytex tsibble urca utf8 vctrs viridis viridisLite withr xfun xts yaml zoo

Plotting Time Series Data

Rendered fromPlotting_Time_Series.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Diagnostic Plots for ARIMA Models	arima_diag
Time Series Cross Correlation Lags Visualization	ccf_plot
Visualization of the Residuals of a Time Series Model	check_res
Coffee Prices: Robusta and Arabica	Coffee_Prices
A Functional Approach for Building the 'train_model' Components	add_horizon add_input add_level add_methods add_train_method add_xreg build_model create_model remove_methods set_error
Crude Oil Prices: Brent - Europe	EURO_Brent
Forecasting simulation	forecast_sim
University of Michigan Consumer Survey, Index of Consumer Sentiment	Michigan_CS
Plot the Models Error Rates on the Testing Partitions	plot_error
Plotting Forecast Object	plot_forecast
Visualizing Grid Search Results	plot_grid
Plot the Models Performance on the Testing Partitions	plot_model
Histogram Plot of the Residuals Values	res_hist
Visualize of the Fitted and the Forecasted vs the Actual Values	test_forecast
Train, Test, Evaluate, and Forecast Multiple Time Series Forecasting Models	train_model
An Interactive Visualization of the ACF and PACF Functions	ts_cor
Visualization of the Decompose of a Time Series Object	ts_decompose
Tuning Time Series Forecasting Models Parameters with Grid Search	ts_grid
Heatmap Plot for Time Series	ts_heatmap
Get the Time Series Information	ts_info
Time Series Lag Visualization	ts_lags
Moving Average Method for Time Series Data	ts_ma
Plotting Time Series Objects	ts_plot
Polor Plot for Time Series Object	ts_polar
Quantile Plot for Time Series	ts_quantile
Transform Time Series Object to Data Frame Format	ts_reshape
Seasonality Visualization of Time Series Object	ts_seasonal
Split Time Series Object for Training and Testing Partitions	ts_split
Summation of Multiple Time Series Objects	ts_sum
3D Surface Plot for Time Series	ts_surface
Transform Time Series Object to Prophet input	ts_to_prophet
US Key Indicators - data frame format	US_indicators
US monthly natural gas consumption	USgas
US Monthly Civilian Unemployment Rate	USUnRate
US Monthly Total Vehicle Sales	USVSales
Converting 'xts' object to 'ts' object	xts_to_ts
Converting 'zoo' object to 'ts' object	zoo_to_ts