adjust_ts_data

Convert a compatible dataset to a <code>ts_data</code> object by setting
column names, class, and the <code>sw</code> attribute consistently.

Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies.
TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation.
Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance.
It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies.
More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.

Eduardo Ogasawara

tspredit

Time Series Prediction with Integrated Tuning

Cristiane Gea

Diego Carvalho

Diogo Santos

Eduardo Bezerra

Esther Pacitti

Fabio Porto

Fernando Alexandrino

Rebecca Salles

Vitoria Birindiba

CEFET/RJ 

adjust_ts_data function

<dl><dt>data</dt>
<dd>Matrix or data.frame to adjust.</dd></dl>

Arguments

Adjust ts_data — adjust_ts_data

<dl>

<dt>data</dt>
<dd>Matrix or data.frame to adjust.</dd>

</dl>

Adjust <code>ts_data</code>

adjust_ts_data: Adjust `ts_data`

Description

Usage

Value

Arguments