[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120052-en":3,"doc-seo-120052-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120052,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Hybridizing Machine Learning with Time Series Analysis for Enhanced Forecasting in Management Science and Operational Efficiency - A Systematic Review","This systematic review examines how machine learning techniques can be integrated with traditional time series analysis to strengthen forecasting and decision-making in management science. By surveying diverse studies and practical implementations, it analyzes hybrid applications across market trend forecasting, inventory management, financial management, and operational efficiency. The review details how feature extraction, hybrid modeling, and predictive analytics improve forecasting accuracy and operational performance while supporting more robust managerial strategies.","Hybridizing Machine Learning with Time Series Analysis for Enhanced Forecasting in Management Science and Operational Efficiency: A Systematic Review  \nAydin Teymourifar 1, Maria A. M. Trindade2  \n1Católica Porto Business School, Centro de Estudos em Gestão e Economia, Porto, Portugal 2SDA Bocconi, School of Management, Milano, Italy  \nAbstract:  \nIn 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.  \nKeywords: Machine learning, Time series analysis, Forecasting, Management science, Operational efficiency.  \nThe integration of machine learning with traditional time series analysis methods has become pivotal in the field of management science. Time series analysis provides crucial insights into sequential data points, aiding managerial decision-making. This study explores the symbiotic relationship between machine learning and time series analysis, investigating its impact on market trend forecasting, inventory management, financial management, and operational efficiency. Through a systematic review, we uncover how this hybrid approach enhances forecasting accuracy and operational efficacy in management practices.  \nIn market trend forecasting, hybrid models that combine machine learning algorithms, such as Neural Networks and Support Vector Machines, with traditional time series models like ARIMA, have proven effective in addressing complex nonlinear patterns and seasonality. The incorporation of feature extraction techniques, such as Fourier or Wavelet Transforms, further enhances forecasting capabilities by extracting meaningful insights from time series data (Cheng Zhang et al., 2022; Dama & Sinoquet, 2021, Ghaderpour et al., 2021) .  \nInventory management experiences enhanced benefits through the integration of machine learning with time series forecasting, facilitating precise predictions of product demands and the optimization of inventory levels. The utilization of techniques like reinforcement learning serves to further refine inventory optimization strategies, resulting in substantial cost savings and efficiency improvements. This integration of machine learning with time series forecasting not only aids in accurately predicting product demands but also in optimizing inventory levels by considering factors like promotional activities and historical sales data. The incorporation of reinforcement learning techniques further refines the optimization of inventory levels based on predictions, ultimately leading to significant cost savings and efficiency improvements (Seyedan & Mafakheri, 2020) .  \nIn financial management, the utilization of machine learning models for analyzing financial time series data plays a pivotal role in risk management and portfolio optimization. The integration of hybrid models, which combine predictive analytics with traditional financial theories, empowers more informed decision-making and enhances risk assessment strategies. Specifically, machine learning models are employe","cbCaijGFI1aAnV9e","https://ap.wps.com/l/cbCaijGFI1aAnV9e","pdf",48593,1,2,"English","en",105,"# Abstract\n## Hybrid integration in forecasting domains\n### Market trend forecasting\n### Inventory management\n### Financial management\n### Operational efficiency\n## Systematic review methodology\n## Data preprocessing, feature extraction, and evaluation","[{\"question\":\"What does the systematic review focus on?\",\"answer\":\"It focuses on combining machine learning with traditional time series analysis to improve forecasting in management science and operational efficiency.\"},{\"question\":\"How are hybrid models used in market trend forecasting?\",\"answer\":\"They combine machine learning algorithms such as neural networks and support vector machines with time series models like ARIMA, including feature extraction methods such as Fourier or wavelet transforms.\"},{\"question\":\"Which areas benefit most from the hybrid approach according to the review?\",\"answer\":\"The review highlights market trend forecasting, inventory management, financial management (risk and portfolio optimization), and operational efficiency such as predictive maintenance and process optimization.\"}]","Hybridizing Machine Learning with Time Series Analysis for Enhanced Forecasting in Management Science and Operational Efficiency - 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