Update 'autocorr.m'

autocorr.m

@@ -0,0 +1,114 @@
+%% This function was automatically generated. When modifying its signature, take care to apply
+%% modifications also to the descriptor files in the repository.
+%% function out = my_app(Time Series File, Number of lags , Number of standard deviations)
+
+function [acf,lags,bounds,numLags,numMA,numSTD] = ...
+          autocorr(time_series,numLags,numMA,numSTD)
+
+[rows,columns] = size(time_series);
+
+if (rows ~= 1) && (columns ~= 1)
+    
+    error('Input file must be a vector');
+      
+end
+
+time_series = time_series(:);         % Ensure a column vector
+N = length(time_series);              % Sample size
+defaultLags = 20;                     % Recommendation of [1]
+
+% Ensure numLags is a positive integer or set default:
+
+if (nargin >= 2) && ~isempty(numLags)
+    
+   if numel(numLags) > 1
+       
+      error('Number of lags must be a scalar value');
+        
+   end
+   
+   if (round(numLags) ~= numLags) || (numLags <= 0)
+       
+      error('Number of lags must be a positive integer');
+        
+   end
+   
+   if numLags > (N-1)
+       
+      error('Number of ACF lags must not exceed the number of observations minus one');
+    % numLags = min(defaultLags,N-1)
+        
+   end
+   
+else
+    
+   numLags = min(defaultLags,N-1); % Default
+   
+end
+
+% Ensure numMA is a nonnegative integer or set default:
+
+if (nargin >= 3) && ~isempty(numMA)
+    
+   if numel(numMA) > 1
+       
+      error('Number of moving average must be a scalar value');
+        
+   end
+   
+   if (round(numMA) ~= numMA) || (numMA < 0)
+       
+      error('Number of moving average must be a positive integer');
+        
+   end
+   
+   if numMA >= numLags
+       
+      error('Number of moving average must be lower than number of lags');
+        
+   end
+   
+else
+    
+   numMA = 0; % Default
+   
+end
+
+% Ensure numSTD is a positive scalar or set default:
+
+if (nargin >= 4) && ~isempty(numSTD)
+    
+   if numel(numSTD) > 1
+       
+      error('Number of standard deviations must be a scalar value');
+        
+   end
+   
+   if numSTD < 0
+       
+      error('Number of standard deviations cannot be negative');
+        
+   end
+   
+else
+    
+   numSTD = 2; % Default
+   
+end
+
+nFFT = 2^(nextpow2(length(time_series))+1);
+F = fft(time_series-mean(time_series),nFFT);
+F = F.*conj(F);
+acf = ifft(F);
+acf = acf(1:(numLags+1)); % Retain non-negative lags
+acf = acf./acf(1); % Normalize
+acf = real(acf);
+
+% Compute approximate confidence bounds using the approach in [1],
+% equations 2.1.13 and 6.2.2, pp. 33 and 188, respectively:
+
+sigmaNMA = sqrt((1+2*(acf(2:numMA+1)'*acf(2:numMA+1)))/N);  
+bounds = sigmaNMA*[numSTD;-numSTD];
+lags = (0:numLags)';
+     
+end

my_app.m

@@ -1,8 +0,0 @@
-%% This function was automatically generated. When modifying its signature, take care to apply
-%% modifications also to the descriptor files in the repository.
-function out = my_app(Time Series File, Number of lags , Number of standard deviations)
-
-%% AUTOCORRELATION_MOD
-%% TODO: Put your application code here
-
-end