Broken line smoothing: a simple method for interpolating and smoothing data series

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:15:29Z
dc.date.available	2014-03-01T01:15:29Z
dc.date.issued	2000	en
dc.identifier.issn	1364-8152	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13542
dc.subject	curve fitting	en
dc.subject	data analysis	en
dc.subject	regression	en
dc.subject	interpolation	en
dc.subject	smoothing	en
dc.subject	scatterplots	en
dc.subject	piecewise linear regression	en
dc.subject	smoothing splines	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Environmental Sciences	en
dc.subject.other	SPLINE FUNCTIONS	en
dc.subject.other	NOISY DATA	en
dc.subject.other	REGRESSION	en
dc.title	Broken line smoothing: a simple method for interpolating and smoothing data series	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S1364-8152(99)00026-2	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S1364-8152(99)00026-2	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	A technique is proposed for smoothing a broken line fit, with known break points, to observational data. It will be referred to as 'broken line smoothing'. The smoothness term is defined by means of the angles formed by the consecutive segments of the broken line, and is given an adjustable weight. The roughness of the resulting broken line can then be controlled by appropriately tuning the weight of smoothness term and the number of straight-line segments. The broken line smoothing can be used for data analysis in several applications as an alternative to other methods such as locally weighted regression and smoothing splines. The mathematical background and details of the method as well as practical aspects of its application are presented and discussed. Also, several examples using both synthesised and real world (hydrological and climatological) data are presented to explore and illustrate the methodology. (C) 2000 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	ENVIRONMENTAL MODELLING & SOFTWARE	en
dc.identifier.doi	10.1016/S1364-8152(99)00026-2	en
dc.identifier.isi	ISI:000086143200002	en
dc.identifier.volume	15	en
dc.identifier.issue	2	en
dc.identifier.spage	139	en
dc.identifier.epage	149	en