Functions for time series exploration, modelling and forecasting for R: tsutils package

Development repository for the tsutils package for R. Stable version can be found at: https://cran.r-project.org/package=tsutils

Installing

To install the development version use:

if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("trnnick/tsutils")

Otherwise, install the stable version from CRAN:

install.packages("tsutils")

Functionality

The tsutils package provides functions to support various aspects of time series and forecasting modelling. In particular this package includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" tools, such as treating time series for trailing and leading values.

Time series exploration:

• cmav: centred moving average.
• coxstuart: Cox-Stuart test for location/dispersion.
• decomp: classical time series decomposition.
• seasplot: construct seasonal plots.
• seastest: seasonal test based on the Friedman test.
• trendtest: test a time series for trend.

Time series modelling:

• getOptK: optimal temporal aggregation level for AR(1), MA(1), ARMA(1,1).
• lagmatrix: create leads/lags of variable.
• residout: construct control chart of residuals.
• seasdummy: create seasonal dummies.
• theta: Theta method.

Hierarchical time series:

• plotS: Generic function to plot hierarchies (S matrix).
• plotSblock: Function to plot S-matrices with block representation.
• plotStree: Function to plot S-matrices with tree representation.
• plotThief: Function to plot temporal hierarchies.
• Saugment: Augment hierarchy with discrepancy series.
• Smean: Mean (equal or unequal weights) aggregation S matrix.
• Sgroup: S matrix from dimensions.
• Sthief: temporal hierarchy S matrix.

Forecasting process modelling:

• abc: ABC analysis.
• xyz: XYZ analysis.
• abcxyz: ABC-XYZ analyses visualisation.

Evaluation

• nemenyi: column-wise Friedman and Nemenyi post-hoc testing.
• nemenyi.sens: sensitivity of the Nemenyi test to the confidence level.

Quality of life:

• geomean: geometric mean.
• lambdaseq: generate sequence of lambda for LASSO regression.
• leadtrail: remove leading/training zeros/NAs.
• wins: winsorisation, including vectorised versions colWins and rowWins.
• colour.blend: emulate transparency colours for EPS exports.

Time series data:

• referrals: A&E monthly referrals.
• gdpchain: Hierarchy of US real GDP in chained dollars.

Authors & contributors

• Nikolaos Kourentzes - (http://nikolaos.kourentzes.com/)
• Ivan Svetunkov - (https://forecasting.svetunkov.ru/)
• Oliver Schaer - (https://scholar.google.com/citations?user=5PoJL3sAAAAJ&hl=en)

References

References are provided where necessary at the help file of each function. The overall modelling philosophy is reflected in:

Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting, 2e. Wessex Press Publishing Co.

License

This project is licensed under the GPL3 License

Happy forecasting!