Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas

We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. MGNG utilizes a rather unknown entropy maximization strategy to control the creation of new neurons in order to focus on frequent sequence patterns. Experimental results demonstrate reduced time complexity compared to MNG while retaining similar accuracy in time series representation. Key words: time series analysis, unsupervised, self-organizing, incremental, recursive temporal context

• Type: Conference
• Year: 2009
• Where: WSOM
• Authors: Andreas Andreakis, Nicolai von Hoyningen-Huene, Michael Beetz
• Keywords: Artificial Intelligence, Growing Neural Gas, Time Series, Time Series Analysis, WSOM 2009