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A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
Electricity consumption forecasting has vital importance for the energy planning of a country.
Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting
The novelty of this study mainly comes from the application of MEMD, which enables the multivariate data decomposition to effectively extract inherent information among relevant variables at different time frequency during the deterioration of multivariate over time.
Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction
A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China.
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks
Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture and the tuning of the hyper-parameters.
Identifying Malicious Web Domains Using Machine Learning Techniques with Online Credibility and Performance Data
Malicious web domains represent a big threat to web users' privacy and security.
Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue
Practical implications: This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains, but also provides an effective resampling approach for handling the class imbal-ance issue in the area of malicious web domain identification.
Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework
Highly accurate interval forecasting of electricity demand is fundamental to the success of reducing the risk when making power system planning and operational decisions by providing a range rather than point estimation.
Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression
As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy.
Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences
Following the "decomposition-and-ensemble" principle, the empirical mode decomposition (EMD)-based modeling framework has been widely used as a promising alternative for nonlinear and nonstationary time series modeling and prediction.
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches.
A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization
Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS).
Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices
An accurate prediction of crude oil prices over long future horizons is challenging and of great interest to governments, enterprises, and investors.
PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research.
