Multi-step time series prediction intervals using neuroevolution

Cortez, Paulo; Pereira, Pedro; Mendes, Rui

doi:10.1007/s00521-019-04387-3

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…This can discard the redundant features (lasso) or decrease them to a minimal value (elastic); on the other hand, these still detect the interactions among features [81,82]. Many studies have compared the performance of the different methods with different classifiers and concluded that there is no such 'perfect feature selection method' for all problem types [75,[83][84][85][86][87]. That is why the feature selection method which was chosen for this study is based on the ensemble method, where the feature selection methods are combined so the different strengths can be combined [88].…”

Section: Feature Selectionmentioning

confidence: 99%

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Mamun

Elfouly

2023

Bioengineering

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Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

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Section: Feature Selectionmentioning

confidence: 99%

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Mamun

Elfouly

2023

Bioengineering

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“…XGBoost is an integrated machine learning algorithm which is on the basis of the decision tree [9,10]. Not only has better accuracy, but also a regularization term is added to enhance the generalization effect of the model.…”

Section: Xgboost Modelmentioning

confidence: 99%

Multi-step Time Series Forecasting Based on Informer-XGBoost-GA

Sun

Chen

Qin³

et al. 2022

J. Phys.: Conf. Ser.

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Multi-step prediction of time series has great significance in practical application. To improve the accurate prediction of multistep time series, time series prediction models have good long-time dependence and are able to analyze the correlation between information in time series. In this paper, a multi-step prediction model on the basis of Informer-XGBoost-GA was proposed, first establishing a combination of Informer and XGBoost, and then optimizing the weight of the combined model through GA. By comparing the experimental results, it can be seen that the proposed that the proposed prediction model significantly outperforms the comparison model and raise the accuracy of the prediction with advantage.

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“…We set the number of MLP architectures used in the NN ensemble to N r = 7, which is also adopted in other multi-step forecasting experiments [69]. Each MLP in the ensemble follows the architecture described in Section 4.3.2 and was trained with 100 epochs of the BFGS algorithm.…”

Section: Modeling Setupmentioning

confidence: 99%

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Gonçalves

Cortez

Carvalho

et al. 2021

Decision Support Systems

Self Cite

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Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventoryrelated costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.

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Multi-step time series prediction intervals using neuroevolution

Cited by 12 publications

References 28 publications

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Multi-step Time Series Forecasting Based on Informer-XGBoost-GA

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

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