Least-squares function approximation: Difference between revisions

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Latest revision as of 01:46, 13 December 2023

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{{Short description|Mathematical method}}

In [[mathematics]], '''least squares function approximation''' applies the principle of [[least squares]] to [[function approximation]], by means of a weighted sum of other functions. The best approximation can be defined as that which minimisesminimizes the difference between the original function and the approximation; for a least-squares approach the quality of the approximation is measured in terms of the squared differences between the two.

<nowiki>#</nowiki>property copyright "Copyright 2016, MetaQuotes Software Corp."

#property link      "<nowiki>http://www.mql5.com</nowiki>"

#property version   "1.00"

#property strict

#property indicator_separate_window

#property indicator_buffers 1

input int     iBarsTotal=300;      

string pair[]={"EURUSD","GBPUSD","AUDUSD","NZDUSD","USDCAD","USDCHF","USDJPY"};

bool bDirect[]={false,false,false,false,true,true,true};

int iCount=7;

//+------------------------------------------------------------------+

//|                                                                  |

//+------------------------------------------------------------------+

double GetValue(int shift)

  {

   double res=1.0,t;

   for(int i=0; i<iCount; i++)

     {

      t=iClose(pair[i],0,shift);

      if(!bDirect[i]) t=1/t;

      res*=t;

     }//end for (int i = 0; i < iCount; i++)

   return (NormalizeDouble(MathPow (res, (double)1/iCount), _Digits) );

  }

double upp[];

//+------------------------------------------------------------------+

//|                                                                  |

//+------------------------------------------------------------------+

int OnInit()

  {

   IndicatorShortName("testBasket");

   IndicatorDigits(_Digits);

   SetIndexStyle(0,DRAW_LINE,STYLE_SOLID,2,clrGreen);

   SetIndexBuffer(0,upp);

   SetIndexLabel(0,"testBasket");

   return(INIT_SUCCEEDED);

  }

//+------------------------------------------------------------------+

//|                                                                  |

//+------------------------------------------------------------------+

int OnCalculate(const int rates_total,

                const int prev_calculated,

                const datetime &time[],

                const double &open[],

                const double &high[],

                const double &low[],

                const double &close[],

                const long &tick_volume[],

                const long &volume[],

                const int &spread[])

  {

   if(prev_calculated==0)

     {

      int total;

      if(iBarsTotal==0) total=rates_total;

      else total= MathMin(iBarsTotal,rates_total);

      for(int i = 0; i<total; i++) upp[i] = GetValue(i);

     }//end if (prev_calculated == 0)

   else

     {

      upp[0]=GetValue(0);

     }

   return(rates_total);

  }

==Functional analysis==

Line 84 ⟶ 14:

with the set of functions {<math>\ \phi _j (x) </math>} an [[Orthonormal_set#Real-valued_functions|orthonormal set]] over the interval of interest, {{nowrap|say [a, b]}}: see also [[Fejér's theorem]]. The coefficients {<math>\ a_j </math>} are selected to make the magnitude of the difference ||{{nowrap|''f'' − ''f''_''n''}}||² as small as possible. For example, the magnitude, or norm, of a function {{nowrap|''g'' (''x'' )}} over the {{nowrap|interval [a, b]}} can be defined by:<ref name=Folland>

{{cite book |title=Fourier analysis and its application |page =69 |chapter=Equation 3.14 |author=Gerald B Folland |chapter-url=https://books.google.com/books?id=ix2iCQ-o9x4C&pg=PA69 |isbn=978-0-8218-4790-29 |publisher=American Mathematical Society Bookstore |year=2009 |edition=Reprint of Wadsworth and Brooks/Cole 1992}}

</ref>

Line 91 ⟶ 21:

where the ‘*’ denotes [[complex conjugate]] in the case of complex functions. The extension of Pythagoras' theorem in this manner leads to [[function space]]s and the notion of [[Lebesgue measure]], an idea of “space” more general than the original basis of Euclidean geometry. The {{nowrap|{ <math>\phi_j (x)\ </math> } }} satisfy [[Orthogonal#Orthogonal_functions|orthonormality relations]]:<ref name=Folland2>

{{cite book |title=Fourier Analysis and Its Applications|page =69 |first1=Gerald B | last1= Folland|url=https://books.google.com/books?id=ix2iCQ-o9x4C&pg=PA69 |isbn=978-0-8218-4790-29 |year=2009 |publisher=American Mathematical Society}}

</ref>

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the ''n''-dimensional [[Pythagorean theorem]]:<ref name=Wood>

{{cite book |title=Statistical methods: the geometric approach |author= David J. Saville, Graham R. Wood |chapter=§2.5 Sum of squares |page=30 |chapter-url=https://books.google.com/books?id=8ummgMVRev0C&pg=PA30 |isbn=0-387-97517-9 |year=1991 |edition=3rd |publisher=Springer}}

</ref>

Line 110 ⟶ 40:

The generalization of the ''n''-dimensional Pythagorean theorem to ''infinite-dimensional&thinsp;'' [[real number|real]] inner product spaces is known as [[Parseval's identity]] or Parseval's equation.<ref name=Folland3>

{{cite book |title=cited work |page =77 |chapter=Equation 3.22 |author=Gerald B Folland |chapter-url=https://books.google.com/books?id=ix2iCQ-o9x4C&pg=PA77 |isbn=978-0-8218-4790-29 |date=2009-01-13 |publisher =American Mathematical Soc. }}

</ref> Particular examples of such a representation of a function are the [[Fourier series]] and the [[generalized Fourier series]].