Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model

Hadid Wahedi; Kacper Wrona; Mads Heltoft; Sarkaft Saleh; Thomas Roum Knudsen; Ulrik Bendixen; Izabela Nielsen; Subrata Saha; Gregers Sandager Borup

doi:10.1007/978-3-031-16407-1_1

Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model

Hadid Wahedi, Kacper Wrona, Mads Heltoft, Sarkaft Saleh^*, Thomas Roum Knudsen, Ulrik Bendixen, Izabela Nielsen, Subrata Saha, Gregers Sandager Borup

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

Abstrakt

Accurate demand forecasting is critical for any small and medium-sized manufacturer. Limited structured data sources commonly prevent small and medium-sized manufacturers from improving forecasting accuracy, affecting overall performance. We classified products, then implemented a hybrid forecasting method and compared the outcome with Exponential smoothing, ARIMA, LSTM, and ANN forecasting techniques. Numerical results demonstrate that a selection of forecasting methods is not independent of product categorization. For slow-moving products, careful consideration is required. The hybrid ARIMA-ANN method can outperform some existing techniques and lead to higher prediction accuracy, by capturing both linear and nonlinear variations.

Originalsprog	Engelsk
Titel	Advances in Production Management Systems. Smart Manufacturing and Logistics Systems : Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings
Redaktører	Duck Young Kim, Gregor von Cieminski, David Romero
Antal sider	8
Forlag	Springer
Publikationsdato	2022
Sider	3-10
ISBN (Trykt)	9783031164064
DOI	https://doi.org/10.1007/978-3-031-16407-1_1
Status	Udgivet - 2022
Begivenhed	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022 - Gyeongju, Sydkorea Varighed: 25 sep. 2022 → 29 sep. 2022

Konference

Konference	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022
Land/Område	Sydkorea
By	Gyeongju
Periode	25/09/2022 → 29/09/2022

Navn	I F I P Advances in Information and Communication Technology
Vol/bind	663 IFIP
ISSN	1868-4238

Bibliografisk note

Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.

Adgang til dokumentet

10.1007/978-3-031-16407-1_1

Andre filer og links

Link to publication in Scopus

Citationsformater

Wahedi, H., Wrona, K., Heltoft, M., Saleh, S., Knudsen, T. R., Bendixen, U., Nielsen, I., Saha, S., & Borup, G. S. (2022). Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. I D. Y. Kim, G. von Cieminski, & D. Romero (red.), Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings (s. 3-10). Springer. I F I P Advances in Information and Communication Technology Bind 663 IFIP https://doi.org/10.1007/978-3-031-16407-1_1

Wahedi, Hadid ; Wrona, Kacper ; Heltoft, Mads et al. / Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings. red. / Duck Young Kim ; Gregor von Cieminski ; David Romero. Springer, 2022. s. 3-10 (I F I P Advances in Information and Communication Technology, Bind 663 IFIP).

@inproceedings{e5b8dafb252c445b8c9684a0869ca93d,

title = "Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model",

abstract = "Accurate demand forecasting is critical for any small and medium-sized manufacturer. Limited structured data sources commonly prevent small and medium-sized manufacturers from improving forecasting accuracy, affecting overall performance. We classified products, then implemented a hybrid forecasting method and compared the outcome with Exponential smoothing, ARIMA, LSTM, and ANN forecasting techniques. Numerical results demonstrate that a selection of forecasting methods is not independent of product categorization. For slow-moving products, careful consideration is required. The hybrid ARIMA-ANN method can outperform some existing techniques and lead to higher prediction accuracy, by capturing both linear and nonlinear variations.",

keywords = "ARIMA-ANN, Forecasting, Machine learning",

author = "Hadid Wahedi and Kacper Wrona and Mads Heltoft and Sarkaft Saleh and Knudsen, {Thomas Roum} and Ulrik Bendixen and Izabela Nielsen and Subrata Saha and Borup, {Gregers Sandager}",

note = "Publisher Copyright: {\textcopyright} 2022, IFIP International Federation for Information Processing.; IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022 ; Conference date: 25-09-2022 Through 29-09-2022",

year = "2022",

doi = "10.1007/978-3-031-16407-1_1",

language = "English",

isbn = "9783031164064",

series = "I F I P Advances in Information and Communication Technology",

publisher = "Springer",

pages = "3--10",

editor = "Kim, {Duck Young} and {von Cieminski}, Gregor and David Romero",

booktitle = "Advances in Production Management Systems. Smart Manufacturing and Logistics Systems",

address = "Germany",

}

Wahedi, H, Wrona, K, Heltoft, M, Saleh, S, Knudsen, TR, Bendixen, U , Nielsen, I , Saha, S & Borup, GS 2022, Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. i DY Kim, G von Cieminski & D Romero (red), Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings. Springer, I F I P Advances in Information and Communication Technology, bind 663 IFIP, s. 3-10, IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Gyeongju, Sydkorea, 25/09/2022. https://doi.org/10.1007/978-3-031-16407-1_1

Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. / Wahedi, Hadid; Wrona, Kacper; Heltoft, Mads et al.

Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings. red. / Duck Young Kim; Gregor von Cieminski; David Romero. Springer, 2022. s. 3-10 (I F I P Advances in Information and Communication Technology, Bind 663 IFIP).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model

AU - Wahedi, Hadid

AU - Wrona, Kacper

AU - Heltoft, Mads

AU - Saleh, Sarkaft

AU - Knudsen, Thomas Roum

AU - Bendixen, Ulrik

AU - Nielsen, Izabela

AU - Saha, Subrata

AU - Borup, Gregers Sandager

PY - 2022

Y1 - 2022

N2 - Accurate demand forecasting is critical for any small and medium-sized manufacturer. Limited structured data sources commonly prevent small and medium-sized manufacturers from improving forecasting accuracy, affecting overall performance. We classified products, then implemented a hybrid forecasting method and compared the outcome with Exponential smoothing, ARIMA, LSTM, and ANN forecasting techniques. Numerical results demonstrate that a selection of forecasting methods is not independent of product categorization. For slow-moving products, careful consideration is required. The hybrid ARIMA-ANN method can outperform some existing techniques and lead to higher prediction accuracy, by capturing both linear and nonlinear variations.

AB - Accurate demand forecasting is critical for any small and medium-sized manufacturer. Limited structured data sources commonly prevent small and medium-sized manufacturers from improving forecasting accuracy, affecting overall performance. We classified products, then implemented a hybrid forecasting method and compared the outcome with Exponential smoothing, ARIMA, LSTM, and ANN forecasting techniques. Numerical results demonstrate that a selection of forecasting methods is not independent of product categorization. For slow-moving products, careful consideration is required. The hybrid ARIMA-ANN method can outperform some existing techniques and lead to higher prediction accuracy, by capturing both linear and nonlinear variations.

KW - ARIMA-ANN

KW - Forecasting

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85140454640&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-16407-1_1

DO - 10.1007/978-3-031-16407-1_1

M3 - Article in proceeding

AN - SCOPUS:85140454640

SN - 9783031164064

T3 - I F I P Advances in Information and Communication Technology

SP - 3

EP - 10

BT - Advances in Production Management Systems. Smart Manufacturing and Logistics Systems

A2 - Kim, Duck Young

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer

T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022

Y2 - 25 September 2022 through 29 September 2022

ER -

Wahedi H, Wrona K, Heltoft M, Saleh S, Knudsen TR, Bendixen U et al. Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. I Kim DY, von Cieminski G, Romero D, red., Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action - IFIP WG 5.7 International Conference, APMS 2022, Proceedings. Springer. 2022. s. 3-10. (I F I P Advances in Information and Communication Technology, Bind 663 IFIP). doi: 10.1007/978-3-031-16407-1_1