Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis

Kazeem Bankole; Felix Hamza-Lup; Ionut E. Iacob

doi:10.1109/ECAI61503.2024.10607440

Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis

Kazeem Bankole
, Felix Hamza-Lup
, Ionut E. Iacob

Georgia Southern University

Research output: Contribution to book or proceeding › Conference article › peer-review

1 Scopus citations

Abstract

We show how to create Artificial Neural Network based models for performing time series exponential-like smoothing and the well-known Holt time series analysis. Our work fares well compared to the well known Holt time series analysis and prediction method, while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models.

Original language	English
Title of host publication	Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9798350371154
ISBN (Print)	9798350371154
DOIs	https://doi.org/10.1109/ECAI61503.2024.10607440
State	Published - Jun 27 2024
Event	16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024 - Iasi, Romania Duration: Jun 27 2024 → Jun 28 2024

Publication series

Name	Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

Conference

Conference	16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024
Country/Territory	Romania
City	Iasi
Period	06/27/24 → 06/28/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Scopus Subject Areas

Process Chemistry and Technology
Artificial Intelligence
Computer Science Applications
Information Systems and Management
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Modeling and Simulation

Keywords

Artificial Neural Networks
prediction
time series analysis

Access to Document

10.1109/ECAI61503.2024.10607440

Cite this

Bankole, K., Hamza-Lup, F., & Iacob, I. E. (2024). Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis. In Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024 (pp. 1-6). (Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECAI61503.2024.10607440

Bankole, Kazeem ; Hamza-Lup, Felix ; Iacob, Ionut E. / Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis. Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 1-6 (Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024).

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title = "Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis",

abstract = "We show how to create Artificial Neural Network based models for performing time series exponential-like smoothing and the well-known Holt time series analysis. Our work fares well compared to the well known Holt time series analysis and prediction method, while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models.",

keywords = "Artificial Neural Networks, prediction, time series analysis",

author = "Kazeem Bankole and Felix Hamza-Lup and Iacob, \{Ionut E.\}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024 ; Conference date: 27-06-2024 Through 28-06-2024",

year = "2024",

month = jun,

day = "27",

doi = "10.1109/ECAI61503.2024.10607440",

language = "English",

isbn = "9798350371154",

series = "Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--6",

booktitle = "Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024",

address = "United States",

}

Bankole, K, Hamza-Lup, F & Iacob, IE 2024, Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis. in Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024. Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024, Iasi, Romania, 06/27/24. https://doi.org/10.1109/ECAI61503.2024.10607440

Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis. / Bankole, Kazeem; Hamza-Lup, Felix ; Iacob, Ionut E.
Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 1-6 (Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis

AU - Bankole, Kazeem

AU - Hamza-Lup, Felix

AU - Iacob, Ionut E.

PY - 2024/6/27

Y1 - 2024/6/27

N2 - We show how to create Artificial Neural Network based models for performing time series exponential-like smoothing and the well-known Holt time series analysis. Our work fares well compared to the well known Holt time series analysis and prediction method, while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models.

AB - We show how to create Artificial Neural Network based models for performing time series exponential-like smoothing and the well-known Holt time series analysis. Our work fares well compared to the well known Holt time series analysis and prediction method, while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models.

KW - Artificial Neural Networks

KW - prediction

KW - time series analysis

UR - https://www.scopus.com/pages/publications/85201178214

U2 - 10.1109/ECAI61503.2024.10607440

DO - 10.1109/ECAI61503.2024.10607440

M3 - Conference article

AN - SCOPUS:85201178214

SN - 9798350371154

T3 - Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

SP - 1

EP - 6

BT - Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

Y2 - 27 June 2024 through 28 June 2024

ER -

Bankole K, Hamza-Lup F , Iacob IE. Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis. In Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 1-6. (Proceedings of the 16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024). doi: 10.1109/ECAI61503.2024.10607440

Artificial Neural Network Models for Time Series Smoothing and Holt Trend Analysis

Abstract

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UN SDGs

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Keywords

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