ts_fil_recursive

Apply recursive linear filtering (ARMA-style recursion) to a
univariate series or each column of a multivariate series. Useful for
smoothing and mitigating autocorrelation.

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_fil_recursive function

<dl><dt>filter</dt>
<dd>smoothing parameter. The larger the value, the greater the smoothing. The smaller the value, the less smoothing, and the resulting series shape is more similar to the original series.</dd></dl>

Arguments

Recursive Filter — ts_fil_recursive

<dl>

<dt>filter</dt>
<dd>smoothing parameter. The larger the value, the greater the smoothing. The smaller the value, the less smoothing, and the resulting series shape is more similar to the original series.</dd>

</dl>

Recursive Filter

ts_fil_recursive: Recursive Filter

Description

Usage

Value

Arguments

Examples