Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm

Chacón, Henry; Koppisetti, Vishwa; Hardage, David; Choo, Kim‐Kwang Raymond; Rad, Paul

doi:10.1016/j.eswa.2023.119983

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Gradient boosting models are alternatives to specialized models, such as long short-term memory network (LSTM) and gated recurrent unit (GRU) [ 31 , 32 ]. Although these models are not ideal for time series forecasting, they are still generally better suited for handling sequential data compared to non-sequential algorithms (such as random forest, SVM, logistic regression, and naive Bayes) [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Clinical Decision Support System for Managing COPD Patients

Pereira,

Antunes,

Rosa

et al. 2023

JPM

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Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients’ future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work’s CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.

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Section: Related Workmentioning

confidence: 99%

Intelligent Clinical Decision Support System for Managing COPD Patients

Pereira,

Antunes,

Rosa

et al. 2023

JPM

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“…Our exploration also includes the consideration of external factors impacting the cryptocurrency market, drawing inspiration from diverse fields where external influences are significant, such as call center arrivals forecasting 13 and energy consumption prediction 14 . Finally, we highlight the ethical dimensions of our work, recognizing the potential impacts on market stability and investor behavior, a concern also evident in other domains such as real-time resource load prediction 15 .…”

Section: Introductionmentioning

confidence: 99%

Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach

Liu,

Wu,

Jiang

et al. 2024

Preprint

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In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.

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