A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series

Vishwakarma, Gajendra K.; Paul, Chinmoy; Elsawah, A. M.

doi:10.1016/j.eswa.2021.115545

Cited by 21 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In real-world applications, data is online or streamed, i.e., data is available over time. In common cases, the statistical properties of the online data also change over time which makes the data non-stationary (Vishwakarma et al, 2021). This type of data can have non-linear trends, multi-seasonality, and irregular fluctuations (Shi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

Mizan

2024

Preprint

View full text Add to dashboard Cite

The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

Mizan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In real-world applications, data is online or streamed, i.e., data is available over time. In common cases, the statistical properties of the online data also change over time which makes the data nonstationary [9]. This type of data can have non-linear trends, multi-seasonality, and irregular fluctuations [10].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Mizan,

Taghipour

2022

Scientia Iranica

View full text Add to dashboard Cite

Real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. The erroneous data causes inaccurate forecasting which leads to business loss. Moreover, the concept drift problem is a known problem in time series forecasting that also results in poor forecasting accuracy. The execution time of a machine learning model is also crucial when it is deployed in a real-time environment. This work presents an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the timevariant windowing technique. A data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. A meta-model is developed in the online forecasting phase which ensures faster execution for incoming data. The model is implemented for the medical workload prediction after testing and comparing with a few other heterogeneous ensemble models. The comparison results show in terms of the root mean squared error, the proposed model performs at least 65.7% better than the heterogeneous stacked ensemble models on the experimental dataset. Moreover, in comparison to the other standalone models considered in this experiment, the ABRE model reduces the prediction error by approximately 73.6%.

show abstract

Multi-scale deep echo state network for time series prediction

Li,

Guo,

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series

Cited by 21 publications

References 35 publications

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Multi-scale deep echo state network for time series prediction

Contact Info

Product

Resources

About