Abstract
This paper presents a method for analyzing changes in information contents of time series based on a combined adaptive approximate similarity detection and temporal modeling using Bregman information. This work extends previous results on using information geometry for musical signals by suggesting a method for optimal model selection using Information Rate (IR) as a measure of an overall model predictability.
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References
Cont, A., Dubnov, S., Assayag, G.: On the Information Geometry of Audio Streams with Applications to Similarity Computing. IEEE Transactions on Audio, Speech and Language Processing 19(4), 837–846 (2011)
Allauzen, C., Crochemore, M., Raffinot, M.: Factor oracle: A new structure for pattern matching. In: Conference on Current Trends in Theory and Practice of Informatics, pp. 295–310 (1999)
Abdallah, S.A., Plumbley, M.D.: Information dynamics: patterns of expectation and surprise in the perception of music. Connection Science 21(2), 89–117 (2009)
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with bregman divergences. Journal of Machine Learning Research 6, 1705–1749 (2005)
Nielsen, F., Boissonnat, J.-D., Nock, R.: On bregman voronoi diagrams. In: Proc. 18th ACM-SIAM Sympos. Discrete Algorithms (2007)
Lefebvre, A., Lecroq, T.: Compror: Compression with a Factor Oracle. In: Proceedings of the Data Compression Conference (2001)
Dubnov, S., Assayag, G., Cont, A.: Audio Oracle: A New Algorithm for Fast Learning of Audio Structures. In: Proceedings of International Computer Music Conference, ICMC (September 2007)
Dubnov, S., Assayag, G., Cont, A.: Audio Oracle analysis of Musical Information Rate. In: Proceedings of IEEE Semantic Computing Conference, ICSC 2011, Stanford (September 2011)
Van Bellegem, S.: Locally stationary volatility modeling, CORE Discussion Papers, Universit catholique de Louvain, Center for Operations Research and Econometrics, CORE (2011)
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Dubnov, S. (2013). Characterizing Time Series Variability and Predictability from Information Geometry Dynamics. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_73
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DOI: https://doi.org/10.1007/978-3-642-40020-9_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40019-3
Online ISBN: 978-3-642-40020-9
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