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- W2503840003 abstract "This thesis addresses the problem of designing short-term forecastingmodels for water demand time series presentingnonlinear behaviour difficult to be fitted with single linearmodels. These behaviours can be identified and classifiedto build specialised models for performing local predictionsgiven an estimated operational regime. Each behavior class isseen as a forecasting operation mode that activates a forecastingmodel. For this purpose we developed a general modularframework with three different implementations: An implementationof a Multi-Model predictor that works with MachineLearning regressors, clustering algorithms, classification,and function approximations with the objective of producingaccurate forecasts for short horizons. The second andthird implementations are hybrid algorithms that use qualitativeand quantitative information from time series. Thequantitative component contains the aggregated magnitudeof each period of time and the qualitative component containsthe patterns associated with modes. For the qualitativecomponent we used a low order Seasonal ARIMA model andfor the qualitative component a k-Nearest Neighbours thatpredicts the next pattern used to distribute the aggregatedmagnitude given by the Seasonal ARIMA. The third implementationis based on the same architecture, assuming theexistence of an accurate activity calendar with a sequence ofworking and rest days, related to the forecast patterns. Thisscheme is extended with a nonlinear filter module for the predictionof pattern mismatches." @default.
- W2503840003 created "2016-08-23" @default.
- W2503840003 creator A5054039992 @default.
- W2503840003 date "2015-12-01" @default.
- W2503840003 modified "2023-09-24" @default.
- W2503840003 title "Time series forecasting based on classificationof dynamic patterns" @default.
- W2503840003 hasPublicationYear "2015" @default.
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