function?[yrvr]=ssa（x1L）

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%?SSA?generates?a?trayectory?matrix?X?from?the?original?series?x1（通過平滑一個長度為L的窗口，SSA生成了原始時間序列的軌跡方程X）
%?by?sliding?a?window?of?length?L.?The?trayectory?matrix?is?aproximated?（軌跡方程是通過奇異值分解估計的）
%?using?Singular?Value?Decomposition.?The?last?step?reconstructs（最后一步通過估計的軌跡矩陣重構時間序列）
%?the?series?from?the?aproximated?trayectory?matrix.?
%The?SSA?applications?include?smoothing?filtering?and?trend?extraction.（SSA應用包括平滑，濾波和趨勢項提取）
%SSA的應用包括平滑、過濾以及趨勢分析

%?x1?Original?time?series?（column?vector?form）?原始時間序列
%?L??Window?length?????????????????????????????窗口長度
%?y??Reconstructed?time?series?????????????????重構時間序列
%?r??Residual?time?series?r=x1-y???????????????殘余時間序列
%?vr?Relative?value?of?the?norm?of?the?approximated?trajectory?matrix?with?respect
%	??to?the?original?trajectory?matrix

%?The?program?output?is?the?Singular?Spectrum?of?x1?（must?be?a?column
%?vector）必須是列向量
%?using?a?window?length?L.?You?must?choose?the?components?be?used?to?reconstruct?
%the?series?in?the?form?[i1i2:ik...iL]?based?on?the?Singular?Spectrum?appearance.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%




%?Step1?:?Build?trayectory?matri