<p>Daily time series from the NN5 forecasting competition.
Data Type: ATM withdrawal amounts. Category: Benchmark. Observations: 735 per series, 111 series.
The dataset contains 111 univariate time series representing daily cash withdrawals from ATMs in England.
Each series includes 735 observations and may contain missing values and multiple seasonal patterns.
Participants were asked to forecast the next 56 values for each series, and performance was evaluated using the mean sMAPE across all series.</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 

NN5 function

<p>A data frame with 735 rows and 111 columns. Each column corresponds to a different univariate daily time series.</p>

Format

NN5 Time Series Competition — NN5

NN5 Time Series Competition

NN5: NN5 Time Series Competition

Description

Usage

Arguments

Format

Details

References

Examples