Rodrigo, Lopez Farias (2015) Time series forecasting based on classification of dynamic patterns. Advisor: Bemporad, Prof. Alberto. Coadvisor: Sopasakis, Prof. Pantelis . pp. 140. [IMT PhD Thesis]
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This thesis addresses the problem of designing short-term forecasting models for water demand time series presenting nonlinear behaviour difficult to be fitted with single linear models. These behaviours can be identified and classified to build specialised models for performing local predictions given an estimated operational regime. Each behavior class is seen as a forecasting operation mode that activates a forecasting model. For this purpose we developed a general modular framework with three different implementations: An implementation of a Multi-Model predictor that works with Machine Learning regressors, clustering algorithms, classification, and function approximations with the objective of producing accurate forecasts for short horizons. The second and third implementations are hybrid algorithms that use qualitative and quantitative information from time series. The quantitative component contains the aggregated magnitude of each period of time and the qualitative component contains the patterns associated with modes. For the qualitative component we used a low order Seasonal ARIMA model and for the qualitative component a k-Nearest Neighbours that predicts the next pattern used to distribute the aggregated magnitude given by the Seasonal ARIMA. The third implementation is based on the same architecture, assuming the existence of an accurate activity calendar with a sequence of working and rest days, related to the forecast patterns. This scheme is extended with a nonlinear filter module for the prediction of pattern mismatches.
|Item Type:||IMT PhD Thesis|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|PhD Course:||Computer Science and Engineering|
|Date Deposited:||02 May 2016 08:10|
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