EUNITE.Reg

Daily holiday and weekday indicators used as regressors in the EUNITE load forecasting competition.
Data Type: Categorical indicators. Category: Benchmark. Observations: 730 (1997–1998).
This dataset provides binary holiday flags and weekday identifiers to support the prediction of daily maximum electrical loads.
It complements the datasets <code>EUNITE.Loads</code> and <code>EUNITE.Temp</code>.
A test set with corresponding regressors for January 1999 is available.

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 

EUNITE.Reg function

A data frame with 730 rows and 3 columns:<dl>
<dt>Holiday</dt>
<dd>Binary indicator (1 = holiday, 0 = regular day).</dd><dt>Weekday</dt>
<dd>Integer encoding (1 = Sunday, ..., 7 = Saturday).</dd><dt>split</dt>
<dd>Split into train and test</dd>
</dl>

Format

EUNITE Competition – Regressors for Load Forecasting — EUNITE.Reg

A data frame with 730 rows and 3 columns:<dl>
<dt>Holiday</dt>
<dd>Binary indicator (1 = holiday, 0 = regular day).</dd>

<dt>Weekday</dt>
<dd>Integer encoding (1 = Sunday, ..., 7 = Saturday).</dd>

<dt>split</dt>
<dd>Split into train and test</dd>


</dl>

EUNITE Competition – Regressors for Load Forecasting

EUNITE.Reg: EUNITE Competition – Regressors for Load Forecasting

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Examples