A new boosting algorithm for improved time-series forecasting with recurrent neural networks

Assaad, Mohammad; Boné, Romuald; Cardot, Hubert

doi:10.1016/j.inffus.2006.10.009

Cited by 125 publications

(70 citation statements)

References 46 publications

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“…The maximum number of iterations and the weight distribution are both required to be initialized in AdaBoot. By grouping the output from the learning algorithms, "weak learners", into a weighted sum F(x) that displays the final voted classifier [74], the original results of AdaBoot prediction is shown in Table A2. …”

Section: Appendix B2 Adaptive Boosting Modelmentioning

confidence: 99%

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

et al. 2017

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Due to the increasing waste generation over the years in China, there is an urgent need to addressing this major problem by implementing effective household waste separation programs. Although past studies have tried to explain the waste behaviour from social and psychological motivations, there is little understanding as to the impact of individual moral obligation and past experience on forming waste separating intention. The aim of this study is to investigate key determinants influencing household waste separation intention and behaviour. Based on the theory of planned behaviour (TPB) model, we designed a survey questionnaire by taking account of determinants including residents' attitude, subjective norm, perceived behavioural control, moral obligation, past behaviour, and demographic factors, with the prediction of household waste separation behaviour in mind. After obtaining 628 valid questionnaires from households in the city of Hangzhou, the partial least squares structural equation modelling (PLS-SEM) was conducted to evaluate the effectiveness of each construct. The results indicate that subjective norms, perceived behavioural control, past behaviour and intention significantly predict household waste separation behaviour, with past behaviour being the most significant construct to predict individuals' intention and behaviour. Additional analysis on the moderating effect of different kinds of people in terms of their genders, ages, income levels and perceived policy effectiveness, to further evoke household waste separation behaviours is also discussed. The findings suggest insightful future policies that can focus on residents' habit formation by providing the convenient location of waste separation and collection facilities, encouraging market-driven recycling programs and traders, promoting community campaigns and education which help residents to form favourable habits for protecting the environment.

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Section: Appendix B2 Adaptive Boosting Modelmentioning

confidence: 99%

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

et al. 2017

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“…The next stage is determining the number of ARMA and ARIMA models parameters that perform by Partial Auto Correlation Function (PACF) and Auto Correlation function (ACF) curves [Assaad et al 2008]. Other parameters that should be determined are d and D, which de-fined for ARIMA models.…”

Section: Arima Modelmentioning

confidence: 99%

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Sutthichaimethee

Ariyasajjakorn

2017

J. Ecol. Eng.

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“…Elliott and Timmermann [6] however dispel the notion that equallyweighted combined forecasts lead to better performance than estimates of optimal forecast combination weights stating that this is directly linked to the use of the mean squared error loss as the loss function. More recently [7] use the weighted median because it is less sensitive to outliers than the weighted mean and gives better results under boosting. Evidence supporting the use of the arithmetic mean is extensive while taking the median over a number of time series forecasting models has emerged as a contender, with mixed results.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of neural network ensembles and model selection for time series prediction

Barrow

Crone

Kourentzes

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data. Several methods have been developed to produce neural network ensembles ranging from taking a simple average of individual model outputs to more complex methods such as bagging and boosting. Which ensemble method is best; what factors affect ensemble performance, under what data conditions are ensembles most useful and when is it beneficial to use ensembles over model selection are a few questions which remain unanswered. In this paper we present some initial findings using neural network ensembles based on the mean and median applied to forecast synthetic time series data. We vary factors such as the number of models included in the ensemble and how the models are selected, whether randomly or based on performance. We compare the performance of different ensembles to model selection and present the results.

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A new boosting algorithm for improved time-series forecasting with recurrent neural networks

Cited by 125 publications

References 46 publications

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

Understanding Household Waste Separation Behaviour: Testing the Roles of Moral, Past Experience, and Perceived Policy Effectiveness within the Theory of Planned Behaviour

Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

An evaluation of neural network ensembles and model selection for time series prediction

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