Fast and simple trend

6 visualizaciones (últimos 30 días)
Azura Hashim
Azura Hashim el 17 de Dic. de 2017
Comentada: Azura Hashim el 18 de Dic. de 2017
Hi,
I need a fast and simple way to calculate the trend of a variable at each point in time for data over the preceding 1 hour. All I need is whether the trend is increasing or decreasing and to what degree. At the moment I am using fitlm to return the slope for each row but have found this to be too slow. Below is a simple example but my application has a much bigger dataset and I need at least an order of magnitude speedup. Appreciate some help please, especially if there are ways to vectorize the calculation. Thank you.
time=[0.2,0.8,0.9,1.1,1.2,1.7,1.8,2.1,2.2];
x=[0.2,0.4,0.5,0.7,1.1,0.7,0.6,1.7,2.1];
slopes=repmat(NaN,length(x),1);
for row=1:length(x)
startrow=find(time >= time(row)-1,1);
%calculate slope if there are more 2 or more data points
if row > startrow
temptime=time(startrow:row);
tempx=x(startrow:row);
mdl = fitlm(temptime,tempx);
slopes(row)=mdl.Coefficients.Estimate(2);
end
end

Respuesta aceptada

the cyclist
the cyclist el 17 de Dic. de 2017
Editada: the cyclist el 17 de Dic. de 2017
You can do the fit directly with matrix operations. It should be roughly a gazillion times faster.
coeffs = [ones(size(temptime')) temptime']\tempx';
slopes(row) = coeffs(2);
There are presumably other efficiencies if you restructure your data ahead such that you do not need to do the transposes, or create the "ones" matrix inside the loop.
  1 comentario
Azura Hashim
Azura Hashim el 18 de Dic. de 2017
Thank you, this worked well!

Iniciar sesión para comentar.

Más respuestas (1)

Jan
Jan el 17 de Dic. de 2017
What about the faster polyfit:
time = [0.2,0.8,0.9,1.1,1.2,1.7,1.8,2.1,2.2];
x = [0.2,0.4,0.5,0.7,1.1,0.7,0.6,1.7,2.1];
slopes = NaN(length(x), 1);
for row = 1:length(x)
startrow = find(time >= time(row)-1,1);
if row > startrow
P = polyfit(time(startrow:row), x(startrow:row), 1);
slopes(row) = P(1);
end
end
If this is still too slow, use a leaner version of polyfit:
function p = LeanPolyFit1(x, y)
V = [x(:), ones(numel(x), 1)];
% Solve least squares problem:
[Q, R] = qr(V, 0);
p = transpose(R \ (transpose(Q) * y(:)));
end

Categorías

Más información sobre Logical en Help Center y File Exchange.

Etiquetas

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by