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Ensemble neural networks for the development of storm surge flood modeling: A comprehensive review
This review paper focuses on the use of ensemble neural networks (ENN) in the
development of storm surge flood models. Storm surges are a major concern in coastal …
development of storm surge flood models. Storm surges are a major concern in coastal …
A weighted LS-SVM based learning system for time series forecasting
TT Chen, SJ Lee - Information Sciences, 2015 - Elsevier
Time series forecasting is important because it can often provide the foundation for decision
making in a large variety of fields. Statistical approaches have been extensively adopted for …
making in a large variety of fields. Statistical approaches have been extensively adopted for …
Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions
Time series analysis has been widely employed in various domains, including finance,
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
Employing local modeling in machine learning based methods for time-series prediction
SF Wu, SJ Lee - Expert Systems with Applications, 2015 - Elsevier
Time series prediction has been widely used in a variety of applications in science,
engineering, finance, etc. There are two different modeling options for constructing …
engineering, finance, etc. There are two different modeling options for constructing …
Short term load forecasting using bootstrap aggregating based ensemble artificial neural network
Background: Short Term Load Forecasting (STLF) can predict load from several minutes to
week plays a vital role to address challenges such as optimal generation, economic …
week plays a vital role to address challenges such as optimal generation, economic …
Gramophone noise detection and reconstruction using time delay artificial neural networks
Gramophone records were the main recording medium for more than seven decades and
regained widespread popularity over the past several years. Being an analog storage …
regained widespread popularity over the past several years. Being an analog storage …
Sensitive time series prediction using extreme learning machine
HB Wang, X Liu, P Song, XY Tu - International Journal of Machine …, 2019 - Springer
Inspired by a multi-granularity and fractal theory, this work mainly focuses on how to
conceive a training and test dataset at different levels under a small dataset in a complex …
conceive a training and test dataset at different levels under a small dataset in a complex …
Model ensembles of artificial neural networks and support vector regression for improved accuracy in the prediction of vegetation conditions and droughts in four …
For improved drought planning and response, there is an increasing need for highly
predictive and stable drought prediction models. This paper presents the performance of …
predictive and stable drought prediction models. This paper presents the performance of …
[PDF][PDF] Indonesian financial data modeling and forecasting by using econometrics time series and neural network
In recent years, many researchers have been using neural network (NN) model as an
instrument of their research. The motivation behind the application of NN is that it can be …
instrument of their research. The motivation behind the application of NN is that it can be …
Period-aware local modelling and data selection for time series prediction
The paper tackles with local models (LM) for periodical time series (TS) prediction. A novel
prediction method is introduced, which achieves high prediction accuracy by extracting …
prediction method is introduced, which achieves high prediction accuracy by extracting …