Rule generation for categorical time series with Markov assumptions

DOI10.1007/S11222-009-9141-ZzbMath1274.62627OpenAlexW2116970134MaRDI QIDQ692942

Christian H. Weiß

Publication date: 6 December 2012

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-009-9141-z

zbMATH Keywords

episode miningcategorical time seriessequential pattern analysisvariable length Markov model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Sequential Pattern Analysis: A Statistical Investigation of Sequence Length and Support

Cites Work

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• The power of amnesia: Learning probabilistic automata with variable memory length
• Discovering patterns in categorical time series using IFS
• Model selection for variable length Markov chains and tuning the context algorithm
• Variable length Markov chains
• A course on point processes
• A survey of temporal data mining
• The Interestingness Paradox in Pattern Discovery
• A universal data compression system

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