SantaFe.D

<p>Univariate time series D from the Santa Fe Time Series Competition.
Data Type: Simulated nonlinear time series. Category: Benchmark. Observations: 100,500.
This benchmark dataset is composed of a four-dimensional nonlinear and non-stationary series.
Competitors were asked to predict the last 500 observations, and performance was evaluated using NMSE.</p>

datasets

benchmark

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 

SantaFe.D function

<p>A data frame with one column and 100,500 rows, containing numeric time series values.</p>

Format

Santa Fe Time Series Competition - Series D — SantaFe.D

Santa Fe Time Series Competition - Series D

SantaFe.D: Santa Fe Time Series Competition - Series D

Description

Usage

Arguments

Format

References

Examples