Vignette for GuessCompx, an R package for empirical estimation of time and memory algorithm complexities

17 Oct 2021 in Viggnette

This post is a demonstration for GuessCompx, an R package for empirical estimation of time and memory algorithm complexities.

This post introduces GuessCompx which is an R package that performs an empirical estimation on the time and memory complexities of an algorithm or a function, and provides a reliable, convenient and simple procedure for estimation process. This package is available on CRAN (https://cran.r-project.org/package=GuessCompx) and its published article is: Agenis-Nevers, M., Bokde, N.D., Yaseen, Z.M. et al. An empirical estimation for time and memory algorithm complexities: newly developed R package. Multimed Tools Appl 80, 2997–3015 (2021). https://doi.org/10.1007/s11042-020-09471-8.

Vignette for ForecastTB, an R package as a testbench for time series forecasting

16 Oct 2021 in Viggnette

This post is a demonstration for ForecastTB, an R package as a testbench for time series forecasting.

The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. This test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided in this package to visualize the comparative performance and accuracy of different forecasting methods. This package is available on CRAN (https://cran.r-project.org/package=ForecastTB) and its published article is: Bokde, N.D.; Yaseen, Z.M.; Andersen, G.B. ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling. Energies 2020, 13, 2578. https://doi.org/10.3390/en13102578.

Vignette for Jaya, an R package for gradient-free Jaya optimization algorithm

15 Oct 2021 in Viggnette

This post is a demonstration for Jaya, an R package for gradient-free Jaya optimization algorithm.

In the year 2015, Prof. R. Venkata Rao from Sardar Vallabhbhai National Institute of Technology Surat, India proposed and published the Jaya optimization algorithm, which is now among the popular ones for solving constrained and unconstrained optimization problems. This algorithm has now solved several real-life problems. In the year 2019, we (Mr. Mayur Kishor Shende and I) developed an R package for this algorithm, named Jaya. In this article, I have discussed the vignette of the package and demonstrated how to use this package. The official webpage for the Jaya package is https://cran.r-project.org/package=Jaya, and it can be cited as Mayur Shende and Neeraj Bokde (2019). Jaya: Jaya, a Gradient-Free Optimization Algorithm. R package version 0.1.9. https://CRAN.R-project.org/package=Jaya

WindCurves - A Tool to Fit Wind Turbine Power Curves

14 Oct 2021 in Viggnette

This post is a demonstration of WindCurves package, which is a Tool to Fit Wind Turbine Power Curves.

This is a Vignettes of R package, WindCurves. The package WindCurves is a tool used to fit the wind turbine power curves. This package is available on CRAN (https://cran.r-project.org/package=WindCurves) and its published article is: Bokde, Neeraj, Andrés Feijóo, and Daniel Villanueva. 2018. “Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function” Applied Sciences 8, no. 10: 1757. https://doi.org/10.3390/app8101757. It can be useful for researchers, data analysts/scientist, practitioners, statistians and students working on wind turbine power curves. The salient features of WindCurves package are:

PSF - an R Package for Pattern Sequence Based Forecasting Algorithm

13 Oct 2021 in Viggnette

This post is a demonstrate an R Package for Pattern Sequence Based Forecasting Algorithm.

This post is an article published in the R journal that introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its usage with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.