som time series clustering and prediction with recurrent neural networks

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SOM time series clustering and prediction with recurrent neural networks. Aymen Cherif , Hubert Cardot , Romuald Bone 2011, Necurocomputing Presented by Chien-Hao Kung 2011/11/3. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. - PowerPoint PPT Presentation

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Research Progress Report

1SOM time series clustering and prediction with recurrent neural networksAymen Cherif , Hubert Cardot , Romuald Bone2011, Necurocomputing

Presented by Chien-Hao Kung2011/11/3

Intelligent Database Systems LabNational Yunlin University of Science and TechnologyIntelligent Database Systems LabN.Y.U.S.T.I. M.12OutlinesMotivationObjectivesMethodologyExperimentsConclusionsCommentsIntelligent Database Systems LabN.Y.U.S.T.I. M.3MotivationLocal models for regression have been the focus of a great deal of attention in the recent years.

Many models have been proposed to cluster time series and they have been combined with several predictors

Intelligent Database Systems LabN.Y.U.S.T.I. M.34ObjectivesThis paper presents an extension for recurrent neural networks applied to local models and a discussion about the obtained results.

Intelligent Database Systems LabN.Y.U.S.T.I. M.4MethodologyFrom global models to local modelsStep1: The time series is embedded into M-dimensional space vectors

Step2:The time series is clustered into sublearning sets.

Step3:Local predictions are performed on each subset.5Intelligent Database Systems LabN.Y.U.S.T.I. M.MethodologyMulti-layer perceptronA multilayer perceptron (MLP) is a feedforward artificial neural network modelStep1: The time series is embedded into M-dimensional space vectors

6Intelligent Database Systems LabN.Y.U.S.T.I. M.MethodologyVQVQ was a method used for reducing a large volume of vectors to a smaller number of distribution.

Self-Organizing Maps(SOM)The SOM has the advantage of being easy to useHowever, since the original Self-Organizing Maps algorithm does not take into account the temporal sequence processing.7Step2: The time series is clustered into sublearning sets

Intelligent Database Systems LabN.Y.U.S.T.I. M.Methodology8Alternative clustering way

Type1:the use of recurrent processing of time signal with recurrent BMU computationTemporal Kohonen Map(TKM)Recursive Self-Organizing Maps(RSOM)

Type2:consists in mapping the temporal dependencies to spatial correlation.Mege Self-Organizing Maps(MSOM)The SOM with Temporal Activity Diffusion(SOMTAD)Step2: The time series is clustered into sublearning setsIntelligent Database Systems LabN.Y.U.S.T.I. M.Methodology9Step3:Local predictions are performed on each subset.MLP as local predictorsThe use of a temporal windows which is precisely the same as the one used in the clustering step.The feedforward nature of the MLP networkThe output calculation and the weights modification are done at the same time step as the learning process.

Intelligent Database Systems LabN.Y.U.S.T.I. M.Methodology10Step3:Local predictions are performed on each subset.RNN as local predictorsOriginal RNN

Back-Propagation Through Time(BPTT)

Intelligent Database Systems LabN.Y.U.S.T.I. M.Methodology11Step3:Local predictions are performed on each subset.RNN as local predictors

Intelligent Database Systems LabN.Y.U.S.T.I. M.Methodology12Step3:Local predictions are performed on each subset.RNN as local predictors

Intelligent Database Systems LabN.Y.U.S.T.I. M.Time seriesSunspots time series

Laser time series

The Mackey-Glass(MG)-17

Experiments13Intelligent Database Systems LabN.Y.U.S.T.I. M.Sunspots time series

Experiments14

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments15Laser time series

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments16MG-17 time series

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments17Experiments on sunspot

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments18Experiments on Laser time series

Intelligent Database Systems LabN.Y.U.S.T.I. M.Experiments19MG-17 time series

Intelligent Database Systems LabN.Y.U.S.T.I. M.20ConclusionsThis paper preferred to use the original SOM algorithm in order to demonstrate the contribution of RNN as a local model.

However, this paper saw that the performance of the model depends on the clustering and also on the nature of the time series.

Intelligent Database Systems LabN.Y.U.S.T.I. M.2021CommentsDrawbackThe paper is useful for time seriesApplicationTime sereisIntelligent Database Systems LabN.Y.U.S.T.I. M.21

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