Dynamic selection of forecast combiners

Sergio, A; Lima, Tiago Pessoa; Ludermir, Teresa B.

doi:10.1016/j.neucom.2016.08.072

Cited by 23 publications

(10 citation statements)

References 49 publications

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“…However, studies like Neumann's [23], comment that adopting a single model can lead to statistical bias and underestimating the real uncertainty underlying the time series. In this way, authors [24]- [30] have been challenged by combining diverse models to present aggregate estimates.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, one of the problems has been to combine single predictors of the time series to enhance the forecasting performance. 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%

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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%

Neural-Based Ensembles for Particulate Matter Forecasting

et al. 2021

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“…Fourth, the posterior probabilities of the best forecaster model (model weights) are computed using the normalized exponential (Softmax) function

with

and

. The Softmax function is a generalization of the logistic function that is often employed in classification and forecasting exercises using traditional machine learning and deep learning methods as a combiner or activation function [38] . The function assigns larger weights to models with smaller forecasting errors, with the weights decaying exponentially the larger the error ( Table 1 – lines 11–13).…”

Section: Methodsmentioning

confidence: 99%

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic

Ashofteh

Bravo²,

Ayuso³

2022

Applied Soft Computing

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“…The Softmax function is a generalization of the logistic function that transforms a K -dimensional vector of arbitrary real values to a k -dimensional vector of real values in the range that adds up to one. The Softmax function is often used in classification and forecasting exercises using traditional, machine learning and deep learning methods as a combiner or an activation function [ 63 ]. The function assigns larger weights to models with smaller forecasting error, with the weights decaying exponentially the larger the error.…”

Section: Methodsmentioning

confidence: 99%

Pricing participating longevity-linked life annuities: a Bayesian Model Ensemble approach

Bravo

2021

Eur. Actuar. J.

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Participating longevity-linked life annuities (PLLA) in which benefits are updated periodically based on the observed survival experience of a given underlying population and the performance of the investment portfolio are an alternative insurance product offering consumers individual longevity risk protection and the chance to profit from the upside potential of financial market developments. This paper builds on previous research on the design and pricing of PLLAs by considering a Bayesian Model Ensemble of single population generalised age-period-cohort stochastic mortality models in which individual forecasts are weighted by their posterior model probabilities. For the valuation, we adopt a longevity option decomposition approach with risk-neutral simulation and investigate the sensitivity of results to changes in the asset allocation by considering a more aggressive lifecycle strategy. We calibrate models using Taiwanese (mortality, yield curve and stock market) data from 1980 to 2019. The empirical results provide significant valuation and policy insights for the provision of a cost effective and efficient risk pooling mechanism that addresses the individual uncertainty of death, while providing appropriate retirement income and longevity protection.

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Dynamic selection of forecast combiners

Cited by 23 publications

References 49 publications

Neural-Based Ensembles for Particulate Matter Forecasting

Neural-Based Ensembles for Particulate Matter Forecasting

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic

Pricing participating longevity-linked life annuities: a Bayesian Model Ensemble approach

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