WordNet
- made smooth by ironing (同)smoothened
- not having been made smooth by having hands run over the surface
- of the margin of a leaf shape; not broken up into teeth
- the act of smoothing; "he gave his hair a quick smooth"
- free from obstructions; "smooth the way towards peace negotiations" (同)smooth out
- make smooth or smoother, as if by rubbing; "smooth the surface of the wood" (同)smoothen
- having a surface free from roughness or bumps or ridges or irregularities; "smooth skin"; "a smooth tabletop"; "smooth fabric"; "a smooth road"; "water as smooth as a mirror"
- lacking obstructions or difficulties; "the bills path through the legislature was smooth and orderly"
- of motion that runs or flows or proceeds without jolts or turbulence; "a smooth ride"
PrepTutorEJDIC
- (表面が)『滑らかな』,すべすべした;『平らな』平坦(へいたん)な / (動きが)円滑な,揺れのない / (物事が)すらすら運ぶ,順調な,平隠な / (味など)滑らかな;(練り粉など)つぶつぶ(むら)のない / 愛想のよい,取り入るような / …‘を'『滑らかにする』,平らにする,〈髪など〉‘を'なでつける《+『out(down)』+『名,』+『名』+『out(down)』》 / 〈物事〉‘を'容易にする,円滑にする《+『away(out)』+『名,』+『away(out)』》 / 〈人・気持ちなど〉‘を'静める,和らげる《+『down』+『名,』+『名』+『down』》 / 滑らか(平ら)になる;隠やかになる《+『down』》 / =smoothly
Wikipedia preview
出典(authority):フリー百科事典『ウィキペディア(Wikipedia)』「2015/07/19 18:06:41」(JST)
[Wiki en表示]
This article is about a type of statistical technique for handling data. For other uses, see Smoothing (disambiguation).
In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. Smoothing may be used in two important ways that can aid in data analysis (1) by being able to extract more information from the data as long as the assumption of smoothing is reasonable and (2) by being able to provide analyses that are both flexible and robust.[1] Many different algorithms are used in smoothing.
Smoothing may be distinguished from the related and partially overlapping concept of curve fitting in the following ways:
- curve fitting often involves the use of an explicit function form for the result, whereas the immediate results from smoothing are the "smoothed" values with no later use made of a functional form if there is one;
- the aim of smoothing is to give a general idea of relatively slow changes of value with little attention paid to the close matching of data values, while curve fitting concentrates on achieving as close a match as possible.
- smoothing methods often have an associated tuning parameter which is used to control the extent of smoothing. Curve fitting will adjust any number of parameters of the function to obtain the 'best' fit.
However, the terminology used across applications is mixed. For example, use of an interpolating spline fits a smooth curve exactly through the given data points and is sometimes called "smoothing".[citation needed]
Contents
- 1 Linear smoothers
- 2 Smoothing algorithms
- 3 See also
- 4 References
- 5 Further reading
Linear smoothers
In the case that the smoothed values can be written as a linear transformation of the observed values, the smoothing operation is known as a linear smoother; the matrix representing the transformation is known as a smoother matrix or hat matrix.[citation needed]
The operation of applying such a matrix transformation is called convolution. Thus the matrix is also called convolution matrix or a convolution kernel. In the case of simple series of data points (rather than a multi-dimensional image), the convolution kernel is a one-dimensional vector.
Smoothing algorithms
One of the most common algorithms is the "moving average", often used to try to capture important trends in repeated statistical surveys. In image processing and computer vision, smoothing ideas are used in scale space representations. The simplest smoothing algorithm is the "rectangular" or "unweighted sliding-average smooth". This method replaces each point in the signal with the average of "m" adjacent points, where "m" is a positive integer called the "smooth width". Usually m is an odd number. The triangular smooth is like the rectangular smooth except that it implements a weighted smoothing function.[2]
Some specific smoothing and filter types are:
- Additive smoothing
- Butterworth filter
- Digital filter
- Kalman filter
- Kernel smoother
- Laplacian smoothing
- Stretched grid method
- Low-pass filter
- Savitzky–Golay smoothing filter based on the least-squares fitting of polynomials to segments of the data
- Local regression also known as "loess" or "lowess"
- Smoothing spline
- Ramer–Douglas–Peucker algorithm
- Moving average a form of average which has been adjusted to allow for seasonal or cyclical components of a time series. Moving average smoothing is a smoothing technique used to make the long term trends of a time series clearer.[3]
- Exponential smoothing used to reduce irregularities (random fluctuations) in time series data, thus providing a clearer view of the true underlying behaviour of the series. It also provides an effective means of predicting future values of the time series (forecasting).[3]
- Kolmogorov–Zurbenko filter
See also
- Convolution
- Curve fitting
- Edge preserving smoothing
- Graph cuts in computer vision
- Numerical smoothing and differentiation
- Scale space
- Statistical signal processing
- Window function
- Subdivision surface, used in computer graphics
References
- ^ Simonoff, Jeffrey S. (1998) Smoothing Methods in Statistics, 2nd edition. Springer ISBN 978-0387947167[page needed]
- ^ O'Haver, T. (2012, Jan.). Smoothing. Retrieved from http://terpconnect.umd.edu/~toh/spectrum/Smoothing.html
- ^ a b Easton, V. J.; & McColl, J. H. (1997)"Time series", STEPS Statistics Glossary
Further reading
- Hastie, T.J. and Tibshirani, R.J. (1990), Generalized Additive Models, New York: Chapman and Hall.
- Einicke, G.A. (2012). Smoothing, Filtering and Prediction: Estimating the Past, Present and Future. Intech. ISBN 978-953-307-752-9.
UpToDate Contents
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English Journal
- Space weather and human deaths distribution: 25 years' observation (Lithuania, 1989-2013).
- Stoupel EG, Petrauskiene J, Kalediene R, Sauliune S, Abramson E, Shochat T.
- Journal of basic and clinical physiology and pharmacology.J Basic Clin Physiol Pharmacol.2015 Sep 1;26(5):433-41. doi: 10.1515/jbcpp-2014-0125.
- BACKGROUND: Human health is affected by space weather component [solar (SA), geomagnetic (GMA), cosmic ray (CRA) - neutrons, space proton flux] activity levels. The aim of this study was to check possible links between timing of human (both genders) monthly deaths distribution and space weather acti
- PMID 26068901
- Understanding weekly cycles in suicide: an analysis of Austrian and Swiss data over 40 years.
- Ajdacic-Gross V1, Tran US2, Bopp M3, Sonneck G4, Niederkrotenthaler T4, Kapusta ND5, Rössler W1, Seifritz E1, Voracek M3.
- Epidemiology and psychiatric sciences.Epidemiol Psychiatr Sci.2015 Aug;24(4):315-21. doi: 10.1017/S2045796014000195. Epub 2014 Apr 23.
- BACKGROUND: Seasonal as well as weekly cycles in suicide have been described, replicated and poorly understood for a long time. In Western countries, suicides are typically least frequent on weekends and most frequent on Mondays and Tuesdays. To improve understanding of this phenomenon a strategy is
- PMID 24759304
- Trends and physiology of common serum biochemistries in children aged 0-18 years.
- Loh TP1, Metz MP.
- Pathology.Pathology.2015 Aug;47(5):452-61. doi: 10.1097/PAT.0000000000000274.
- The aim of this study was to visually present and discuss in detail the physiological trends of 22 serum biochemistries in children aged 0-18.A data-mining, LMS (lambda, mu, and sigma) approach was employed to derive the smoothed continuous serum biochemistry centile charts, after application of str
- PMID 26126034
Japanese Journal
- An Efficient Boundary Handling with a Modified Density Calculation for SPH
- メッシュフリー法における最小二乗法による内挿を用いた移流項の処理手法に関する研究
Related Links
- ... バンク」の記事「平滑移動平均線を使いHeiken Ashi Maよりダマシを少なくしたHeiken Ashi Smoothed 」は削除しました。 詳しいことは以下の記事をご覧ください。 「ザイFX!×メタトレーダー(MT4)」が2015年7月1日 、リニューアル ...
- 平均足を更に使い易くしたテクニカル、 それが”Heiken Ashi Smoothed”です。 視覚的にも分かりやすいので、FX初心者にオススメのテクニカルですよ! Heiken Ashi Smoothedとは? 前回の記事で、 平均足について詳しく解説しました。
Related Pictures
★リンクテーブル★
[★]
滑らかな、平らな、滑面の、スムーズな、スムースな
- 関
- even、flat、plane、smooth-surfaced、smoothly