Call for Papers in Special Issue “Data Driven Approaches for Environmental Sustainability 2023” at Energies Journal
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Dear Colleagues,
Google Scholar profile reached 1000 citations!
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Thank you, Google for the delightful email.
VedicDateTime - An R Package to Implement Vedic Calendar System
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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).
cleanTS - a tool funded by Google Summer of Code (GSoC) - 2021 is now published in Neurocomputing (Elsevier) journal
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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.
Google Summer of Code (GSoC) - 2022
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Contributors are invited to Google Summer of Code (GSoC) - 2022!!!
Update on 21-05-2022
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.
Collection of my ‘Data Science’ and ‘Research’ related articles on Medium
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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.
Vignette for GuessCompx, an R package for empirical estimation of time and memory algorithm complexities
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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
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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
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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
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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: