ts_fil_ses

Exponential smoothing focused on the level component, with
optional extensions to trend/seasonality via Holt–Winters variants.

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_ses function

<dl><dt>gamma</dt>
<dd>If TRUE, enables the gamma seasonality component.</dd></dl>

Arguments

Simple Exponential Smoothing — ts_fil_ses

<dl>

<dt>gamma</dt>
<dd>If TRUE, enables the gamma seasonality component.</dd>

</dl>

Simple Exponential Smoothing

ts_fil_ses: Simple Exponential Smoothing

Description

Usage

Value

Arguments

References

Examples