Predictive Modular Neural Networks for Unsupervised Segmentation of Switching Time Series : The Data Allocation Problem  

Oleh:

Kehagias, A. ; Petridis, V.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 6 (2002), page 1432-1449.
Topik: TIME SERIES; modular; neural networks; time series; data allocation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we explore some aspects of the problem of online unsupervised learning of a switching time series, i. e., a time series which is generated by a combination of several alternately activated sources. This learning problem can be solved by a two - stage approach : 1) separating and assigning each incoming datum to a specific dataset (one dataset corresponding to each source) and 2) developing one model per dataset (i. e., one model per source). We introduce a general data allocation (DA) methodology, which combines the two steps into an iterative scheme : existing models compete for the incoming data ; data assigned to each model are used to refine the model. We distinguish between two modes of DA : in parallel DA, every incoming datablock is allocated to the model with lowest prediction error; in serial DA, the incoming datablock is allocated to the first model with prediction error below a prespecified threshold. We present sufficient conditions for asymptotically correct allocation of the data. We also present numerical experiments to support our theoretical analysis.

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