@inproceedings{malinowski_1d-sax_2013,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {1d-{SAX}: {A} {Novel} {Symbolic} {Representation} for {Time} {Series}},
	isbn = {978-3-642-41398-8},
	shorttitle = {1d-{SAX}},
	doi = {10.1007/978-3-642-41398-8_24},
	abstract = {SAX (Symbolic Aggregate approXimation) is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases.},
	language = {en},
	booktitle = {Advances in {Intelligent} {Data} {Analysis} {XII}},
	publisher = {Springer},
	author = {Malinowski, Simon and Guyet, Thomas and Quiniou, René and Tavenard, Romain},
	editor = {Tucker, Allan and Höppner, Frank and Siebes, Arno and Swift, Stephen},
	year = {2013},
	keywords = {Average Approximation Error, Dynamic Time Warping Distance, Original Time Series, Symbolic Representation, Time Series},
	pages = {273--284},
}

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