ts_regsw

Base class for time series regression models built on
sliding-window representations.

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 

ts_regsw function

<dl><dt>preprocess</dt>
<dd>Normalization preprocessor (e.g., <code>ts_norm_gminmax()</code>).</dd>
<dt>input_size</dt>
<dd>Integer. Number of lagged inputs per example.</dd></dl>

Arguments

TSRegSW — ts_regsw

<dl>

<dt>preprocess</dt>
<dd>Normalization preprocessor (e.g., <code>ts_norm_gminmax()</code>).</dd>


<dt>input_size</dt>
<dd>Integer. Number of lagged inputs per example.</dd>

</dl>

TSRegSW

ts_regsw: TSRegSW

Description

Usage

Value

Arguments

Details

Examples