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.
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
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:
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
ets, well-known time series forecasting functions available on CRAN repository.