Copulas-based time series combined forecasters

Oliveira, Ricardo T. A. de; Assis, Thaíze Fernandes O. de; Firmino, Paulo Renato Alves; Ferreira, Tiago A. E.

doi:10.1016/j.ins.2016.10.022

Cited by 15 publications

(7 citation statements)

References 37 publications

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“…Statistically, these ensembles are generally superior in comparison with individual models in terms of both accuracy and efficiency [24], [31]- [34]. Also, among ANN-based approaches, there are ensembles that combine models to obtain a more statistically robust system, outperforming the single forecasting models [12], [29], [35], [36].…”

Section: Introductionmentioning

confidence: 99%

“…The combination methods employed in the ensembles can be divided into two classes: non-trainable [36] and trainable [29]. The non-trainable combination operators commonly used are descriptive statistics, such as mean, median, mode, maximum, and minimum [36].…”

Section: Introductionmentioning

confidence: 99%

“…The non-trainable combination operators commonly used are descriptive statistics, such as mean, median, mode, maximum, and minimum [36]. In turn, the trainable combination methods require a phase of prior estimation of parameters that aims to find the best function of aggregation of the forecasts [29].…”

Section: Introductionmentioning

confidence: 99%

“…In such a context, the present work investigates the performance of 16 forecasting models for PM concentration. As single models we applied the Autoregressive model (AR) [40], Autoregressive and Moving Average model (ARMA) [40], Infinite Impulse Response Filters [41], Multilayer Perceptron (MLP) [20], Radial Basis Function Networks (RBF) [42], Extreme Learning Machines (ELM) [43], Echo State Networks (ESN) [44], and Adaptive Network Fuzzy Inference System (ANFIS) [45]; as non-trainable ensembles, it is considered the mean and median [36]; as trainable ensembles we addressed a linear regression with (LR-FS) and without features selection (LR) [46], ELM with and without the coefficient of regularization (CR), MLP [37], and normal Copula-based models [25], [29]. These combination methods have emerged among the most promising from the time series forecasting literature [12], [29], [35], [37], [38].…”

Section: Introductionmentioning

confidence: 99%

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Neural-Based Ensembles for Particulate Matter Forecasting

et al. 2021

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The air pollution caused by particulate matter (PM) has become a public health issue due to the risks to human life and the environment. The PM concentration in the air causes haze and affects the lungs and the heart, leading to reduced visibility, allergic reactions, pneumonia, asthma, cardiopulmonary diseases, lung cancer, and even death. In this context, the development of systems for monitoring, forecasting, and controlling emissions plays an important role. The literature about forecasting systems based on Artificial Neural Networks (ANNs) ensembles has been highlighted regarding statistical accuracy and efficiency. In this article, trainable and non-trainable combination methods are used for PM 10 and PM 2.5 (particles with an aerodynamic diameter less than 10 and 2.5 micrometers, respectively) time series forecasting for eight different locations, in Finland and Brazil, for different periods. Trainable ensembles based on ANNs, linear regression, and Copulas are compared with non-trainable combinations (mean and median), single ANNs, and linear statistical approaches. Different models are considered so far, including Autoregressive model (AR), Autoregressive and Moving Average Model (ARMA), Infinite Impulse Response Filters (IIR), Multilayer Perceptron (MLP), Radial Basis Function Networks (RBF), Extreme Learning Machines (ELM), Echo State Networks (ESN), and Adaptive Network Fuzzy Inference System (ANFIS). The use of ANNs ensembles, mainly combined with MLP, leads to a better one step ahead forecasting performance. The use of robust air pollution forecasting tools is prime to assist governments in managing air pollution issues like hospital collapse during adverse air quality situations. In this sense, our study is indirectly related to the following United Nations sustainable development goals: SDG 3 -good health and well-being and SDG 11 -sustainable cities and communities.INDEX TERMS Forecasting, particulate matter, artificial neural networks, ensemble.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural-Based Ensembles for Particulate Matter Forecasting

et al. 2021

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“…Diagnostic: the proposal models and their residues are examined to verify their adequacy to the analyzed phenomenon. Further aforementioned models, the use of Artificial Neural Networks (ANN) (RNA) [25,26], and of Copulas Formalism [27] also conquered space among predictive techniques. The last model is conceived as one of the most advanced model used in researches involving predictive modelling.…”

Section: Resultsmentioning

confidence: 99%

Preditive Models And Health Sciences: A Brief Analysis

et al. 2017

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Background: Predictive Models are an important tool in event predicting and health planning. Despite this, there are few works focusing this area. Thus, the analysis of the real benefits of these models in Health Sciences is necessary to be performed. Results:Predictive techniques largely evolved in second half of XX century. The development of AR, MA, ARMA, ARIMA and SARIMA models, through Box-Jenkins methodology, constitute a robust conjunct of mechanisms able to help in solution of epidemiological modeling problems, mainly in Health Sciences, once it allows to evaluate individual characteristics of living beings and its correlation with pathologies in the same space-time. Nevertheless, AR, MA and ARMA does not have tendency in seasonality, which weakens the analysis. Conclusions:To predict the natural history of endemic/epidemic and its health-disease processes in a determined population is a sine que non condition to its adequate management in Public Health context and in adoption of affirmative measures concerning health promotion. Thus, the predictive models, with emphasis in ARIMA, SARIMA, Artificial Neural Networks and Formalism of Copulas are alternatives that can be feasible.Preditive Models and Health Sciences: a Brief Analysis

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A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine

Deng

Yokoyama

et al. 2021

Developments in Advanced Control and Intelligent Automation for Complex Systems

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Copulas-based time series combined forecasters

Cited by 15 publications

References 37 publications

Neural-Based Ensembles for Particulate Matter Forecasting

Neural-Based Ensembles for Particulate Matter Forecasting

Preditive Models And Health Sciences: A Brief Analysis

A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine

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