Intelligent Database Systems Lab 國國國國國國國國 National Yunlin University of Science and Technology 1 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
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SOM time series clustering and prediction with recurrent neural networks
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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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
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SOM time series clustering and prediction with recurrent neural networks
· Local 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
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Objectives
· This paper presents an extension for recurrent neural networks applied to local models and a discussion about the obtained results.
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· From global models to local models─ Step1: 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.
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· Multi-layer perceptron─ A multilayer perceptron (MLP) is a feedforward
artificial neural network model
Step1: The time series is embedded into M-dimensional space vectors
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─ VQ 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 use─ However, since the original Self-Organizing Maps
algorithm does not take into account the temporal sequence processing.
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Step2: The time series is clustered into sublearning sets
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· Alternative clustering way
- Type1:the use of recurrent processing of time signal with recurrent BMU computation Temporal 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 sets
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Step3:Local predictions are performed on each subset.
· MLP as local predictors─ The use of a temporal windows which is precisely the
same as the one used in the clustering step.─ The feedforward nature of the MLP network─ The output calculation and the weights modification
are done at the same time step as the learning process.
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Step3:Local predictions are performed on each subset.
· RNN as local predictors─ Original RNN
─ Back-Propagation Through Time(BPTT)
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Step3:Local predictions are performed on each subset.
· RNN as local predictors
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Step3:Local predictions are performed on each subset.
· RNN as local predictors
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· Time series─ Sunspots time series
─ Laser time series
─ The Mackey-Glass(MG)-17
Experiments
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· Sunspots time series
Experiments
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· Laser time series
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· MG-17 time series
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· Experiments on sunspot
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· Experiments on Laser time series
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· MG-17 time series
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Conclusions· This 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.