Time Series Prediction with Integrated Tuning.

TSPredIT

TSPredIT (Time Series Prediction with Integrated Tuning) is a framework for time series prediction with automatic preprocessing and hyperparameter optimization. It is built on top of the DAL Toolbox and enhances its capabilities by integrating several advanced functionalities:

• Automatic hyperparameter tuning for models and preprocessing
• Outlier detection and removal
• Time series data augmentation
• Filtering techniques for noise reduction
• Ensemble learning support
• Modular and extensible workflow for predictive modeling

TSPredIT is designed to provide a more flexible and customizable pipeline for building predictive models on time series data, making it easier to compare alternatives and automate repetitive tasks.

Examples

Examples of TSPredIT usage are available in the official GitHub repository:

• Time Series examples
• Augmentation examples
• Filter examples

Additional documentation and tutorials for the underlying DAL Toolbox can be found at:

• DAL Toolbox documentation

Installation

The latest version of TSPredIT is available on CRAN:

install.packages("tspredit")

You can install the development version from GitHub:

library(devtools)
devtools::install_github("cefet-rj-dal/tspredit", force = TRUE, upgrade = "never")

Bug reports and feature requests

To report issues or suggest improvements, please open a ticket here:

https://github.com/cefet-rj-dal/tspredit/issues.