Trade-off Clustering Approach for Multivariate Multi-Step Ahead Time-Series Forecasting

Konstandinos Aiwansedo, Wafa Badreddine, Jérôme Bosche

2023

Abstract

Time-Series forecasting has gained a lot of steam in recent years. With the advent of Big Data, a considerable amount of data is more available across multiple fields, thus providing an opportunity for processing historical business-oriented data in an attempt to predict trends, identify changes and inform strategic decision-making. The abundance of time-series data has prompted the development of state-of-the-art machine learning algorithms, such as neural networks, capable of forecasting both univariate and multivariate time-series data. Various time-series forecasting approaches can be implemented when leveraging the potential of deep neural networks. Determining the upsides and downsides of each approach when presented with univariate or multivariate time-series data, thus becomes a crucial matter. This evaluation focuses on three forecasting approaches: a single model forecasting approach (SMFA), a global model forecasting model (GMFA) and a cluster-based forecasting approach (CBFA). The study highlights the fact that the decision pertaining to the finest forecasting approach often is a question of trade-off between accuracy, execution time and dataset size. In this study, we also compare the performance of 6 deep learning architectures when dealing with both univariate and multivariate time-series datasets for multi-step ahead time-series forecasting, across 6 benchmark datasets.

Paper Citation

in Harvard Style

Aiwansedo K., Badreddine W. and Bosche J. (2023). Trade-off Clustering Approach for Multivariate Multi-Step Ahead Time-Series Forecasting. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 137-148. DOI: 10.5220/0011660100003393

in Bibtex Style

@conference{icaart23,
author={Konstandinos Aiwansedo and Wafa Badreddine and Jérôme Bosche},
title={Trade-off Clustering Approach for Multivariate Multi-Step Ahead Time-Series Forecasting},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={137-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011660100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Trade-off Clustering Approach for Multivariate Multi-Step Ahead Time-Series Forecasting
SN - 978-989-758-623-1
AU - Aiwansedo K.
AU - Badreddine W.
AU - Bosche J.
PY - 2023
SP - 137
EP - 148
DO - 10.5220/0011660100003393