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tspredit  

Time Series Prediction Integrated Tuning
View on CRAN: Click here


Download and install tspredit package within the R console
Install from CRAN:
install.packages("tspredit")

Install from Github:
library("remotes")
install_github("cran/tspredit")

Install by package version:
library("remotes")
install_version("tspredit", "1.0.787")



Attach the package and use:
library("tspredit")
Maintained by
Eduardo Ogasawara
[Scholar Profile | Author Map]
First Published: 2023-07-19
Latest Update: 2023-12-22
Description:
Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-driven models (either statistical or machine learning) demand tuning. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data pre-processing that complies with the chosen model. Many time series frameworks have features to build and tune models. The package differs as it provides a framework that seamlessly integrates tuning data pre-processing activities with the building of models. The package provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, tuning, modeling, prediction, and accuracy assessment. More information is available at Izau et al. <doi:10.5753/sbbd.2022.224330>.
How to cite:
Eduardo Ogasawara (2023). tspredit: Time Series Prediction Integrated Tuning. R package version 1.0.787, https://cran.r-project.org/web/packages/tspredit. Accessed 02 Apr. 2025.
Previous versions and publish date:
1.0.707 (2023-07-19 17:20), 1.0.727 (2023-11-02 19:50), 1.0.737 (2023-11-09 17:40), 1.0.747 (2023-12-22 06:00), 1.0.767 (2024-03-26 03:20), 1.0.777 (2024-07-29 16:20)
Other packages that cited tspredit R package
View tspredit citation profile
Other R packages that tspredit depends, imports, suggests or enhances
Complete documentation for tspredit
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