Repository logo
 
Publication

Hybridizing machine learning with time series analysis for enhanced forecasting in management science and operational efficiency: a systematic review

dc.contributor.authorTeymourifar, Aydin
dc.contributor.authorTrindade, Maria A. M.
dc.date.accessioned2024-07-26T09:31:21Z
dc.date.available2024-07-26T09:31:21Z
dc.date.issued2024-07
dc.description.abstractIn the dynamic landscape of management science, this systematic review provides a comprehensive exploration of the amalgamation of machine learning techniques with traditional time series analysis methods. As time series analysis continues to play an increasingly pivotal role in enhancing managerial decision-making processes by offering insights derived from sequential data points, this study endeavors to shed light on the multifaceted applications and synergistic benefits resulting from the integration of time series analysis with machine learning. By scrutinizing a diverse array of studies and practical implementations, the study aims to illuminate the rich potential of this hybrid approach across various domains, including market trend forecasting, inventory management, financial management, and operational efficiency. Through an in-depth analysis, this review elucidates how the fusion of machine learning and time series analysis contributes to heightened forecasting accuracy and operational efficacy, thus empowering decision-makers with more robust insights and strategies.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.14/45881
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.subjectMachine learningpt_PT
dc.subjectTime series analysispt_PT
dc.subjectManagement sciencept_PT
dc.subjectOperational efficiencypt_PT
dc.titleHybridizing machine learning with time series analysis for enhanced forecasting in management science and operational efficiency: a systematic reviewpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSpainpt_PT
oaire.citation.title10th International Conference on Time Series and Forecastingpt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
102954481.pdf
Size:
47.45 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.44 KB
Format:
Item-specific license agreed upon to submission
Description: