Hybrid regression model for near real-time urban water demand forecasting

DOI10.1016/j.cam.2016.02.009zbMath1353.62136OpenAlexW2279630689MaRDI QIDQ313669

Joaquín Izquierdo, Bruno M. Brentan, Edevar jun. Luvizotto, Manuel Herrera, Rafael Pérez-García

Publication date: 12 September 2016

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2016.02.009

zbMATH Keywords

Fourier seriesdemand forecastingnear real-time algorithmssupport vector regressionwater supply

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

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Uses Software

• LIBSVM

Cites Work

• Unnamed Item
• Time series forecasting using a hybrid ARIMA and neural network model
• A hybrid PSO optimized SVM-based model for predicting a successful growth cycle of the \textit{Spirulina platensis} from raceway experiments data
• Investigating asymptotic properties of vector nonlinear time series models
• On the construction of a nonlinear recursive predictor
• Forecasting nonlinear time series with a hybrid methodology
• Optimization algorithms of operative control in water distribution systems
• Online multiple kernel classification
• Municipal Water Demand Forecasting: Tools for Intervention Time Series

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