This post is to demonstrate an R Package for implementing Vedic Calendar System
The VedicDateTime package provides a platform for the Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and is helpful in examining its impact in the time series analysis domain. The background is described in Neeraj Dhanraj Bokde et al. (2021), Karanam L. Ramakumar et al. (2011), [K. S. Charak et al. (2012], (https://www.amazon.in/Elements-Vedic-Astrology-K-S-Charak/dp/8190100807), Satish BD et al. (2013).
This post is an update on my recent publication on cleanTS package
I am glad to share that our R package cleanTS is now accepted for publication in Neurocomputing (Elsevier) journal. This is a recent package joining the league of Automated Machine Learning (AutoML) tools contributed by me and my teams. While working on huge and voluminous time-series datasets, I felt that there is a need to automate the processes of data cleaning, and this led to the concept of cleanTS.
I am glad to share that all three proposed project in Google Summer of Code (GSoC) - 2022 with ‘R Project for Statistical Computing’ organization are accepted. I must congratulate the contributors for their efforts in submitting outstanding proposals for these projects. Those who could not shortlist this time, I wish a huge success for them and hope to connect again next year for the advanced projects.
This post is a collection of my articles published on Medium which are majorly related to Time Series Analysis, Data Science, and Research.
My research work is majorly moving around Data Science, Time Series Analysis and their applications in different domains. While doing so, I came across several difficulties, problems, and possible solutions. I have been collaborating with many professionals, which involves Professors, Researchers, Data Scientists, and Managers among others. Each professional had his distinct skills and I have tried to grab some of them. Based on my experience, I have tried to document my thoughts and research directions in the form of Medium stories. This is my Medium writer profile: https://neerajdhanraj.medium.com/. You may follow me on Medium for regular updates.
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
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.
