CATS

<p>Univariate time series from the CATS (Competition on Artificial Time Series) benchmark.
Data Type: Artificial time series with missing blocks. Category: Benchmark. Observations: 5,000 (4,900 known, 100 missing).
The dataset contains five non-consecutive blocks of 20 missing values each. Competitors were asked to predict these 100 unknown points, and performance was evaluated using MSE (E1 for all unknowns and E2 for the first 80 points).</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 

CATS function

<p>A data frame with five columns and 980 rows. Each column represents a known segment of the time series.</p>

Format

CATS Time Series Competition — CATS

CATS Time Series Competition

CATS: CATS Time Series Competition

Description

Usage

Arguments

Format

Details

References

Examples