TSPred Package for R : Functions for Benchmarking Time Series Prediction

Student: Rebecca Pontes Salles (rebeccapsalles@acm.org)

Advisor: Eduardo Ogasawara (eogasawara@ieee.org)

Description: Functions for time series prediction and accuracy assessment using automatic linear modeling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Available at CRAN: https://CRAN.R-project.org/package=TSPred

Code repository at Git-Hub: https://github.com/RebeccaSalles/TSPred

Reference manual: TSPred.pdf

Acknowledgements: The authors thank CNPq for partially sponsoring this work.

About Eduardo Ogasawara
I am a Professor of the Computer Science Department of the Federal Center for Technological Education of Rio de Janeiro (CEFET / RJ) since 2010. I hold a PhD in Systems Engineering and Computer Science at COPPE / UFRJ. Between 2000 and 2007 I worked in the Information Technology (IT) field where I acquired extensive experience in workflows and project management. I have solid background in the Databases and my primary interest is Data Science. He currently studies space-time series, parallel and distributed processing, and data preprocessing methods. I am a member of the IEEE, ACM, INNS, and SBC. Throughout my career I have been presenting consistent number of published articles and projects approved by the funding agencies, such as CNPq and FAPERJ. I am also reviewer of several international journals, such as VLDB Journal, IEEE Transactions on Service Computing and The Journal of Systems and Software. Currently, I am heading the Post-Graduate Program in Computer Science (PPCIC) of CEFET / RJ.

Comments are closed.