I'm looking to find the n largest values in an array, then to use the indices of those found values as a look up into another array representing time. But I am wondering how I can plot this if i want time to display as a continuous variable. Do I need to zero out data? That wouldn't be preferable for my use case as I'm looking to save memory.
Let's say that I have array A, which is where I am looking for the max values. Then I have array T, which represents timestamps. I want my plot to display continuous time and plot() doesn't like arguments of differing size. How do most people deal with this?
Here's what I've got so far:
numtofind = 4;
A = m{:,10};
T = ((m{:,4} * 3600.0) + (m{:,5} * 60.0) + m{:,6});
[sorted, sortindex] = sort(A(:), 'descend');
maxvalues = sorted(1:numtofind);
maxindex = sortindex(1:numtofind);
corresponding_timestamps = T(maxindex);
%here i plot the max values against time/corresponding timestamps,
%but i want to place them in the right timestamp and display time as continuous
%rather than the filtered set:
plot(time_values, maxvalues);
When you say "time as continuous", do you mean you want time going from minimum to maximum? If so, you can just sort corresponding_timestamps and use that to reorder maxvalues. Even if you don't do that, you can still do plot(time_values, maxvalues, '.') to get a scatter plot which won't mess up your graph with lines.
Related
I am currently new to matlab, and I am trying to do a loop over fifty elements at a time instead of one element at a time. For example, I have a list of 1000 elements, and I would like to compute the sum for every fifty elements. Instead of doing a sum function through indexing, it would be much faster with a loop. How would I go about doing this?
I.e. [1,...50th element, 51th element... 100...]
Output would be the the sum values of 1:50, 51:100, 101:150... and so on.
Thanks in advance
I'm not really sure what you mean by "a sum function through indexing", but there are various ways to do this. In general I try to avoid explicit loops in Matlab and let MathWorks functions do their magic.
results = zeros(20,1);
for i = 1:20
results(i) = sum(1 + (50 * (i - 1)):50 + 50 * (i - 1));
end
Another option is to do something like arrayfun.
sIndex = 1:50:951;
eIndex = 50:50:1000;
result = arrayfun(#(x, y) sum(x:y), sIndex, eIndex);
You could also use reshape and sum to do it one shot.
numbers = 1:1000;
numbers2 = reshape(numbers, 50, []);
result = sum(numbers2);
This last method is what I personally would say is a Matlab way of doing it. arrayfun is basically a wrapper around a loop and the loop is...well a loop.
In case you need the sum, you can also use movsum:
array = 1:1000;
win = 50; % window size
msum = movsum(array,win,'Endpoints','discard');
in the same way, you can use:
movmax Moving maximum
movmean Moving mean
movmedian Moving median
movmin Moving minimum
movstd Moving standard deviation
movvar Moving variance
Using cumsum and diff you can obtain the desired result.
C = [0 cumsum(a)];
out = diff(C(1:50:end));
as a part of a bigger script i want to store data from a while loop in a matrix. I want to save parts of the COG_Ton_Av matrix which is 1738x3 in a new matrix. The COG_Ton_Av changes within every loop so i want to store the results outside. I have found multiple entries on how to store the data in a vector, but nothing for a matrix. What i tried is :
valuesforts= zeros(1000,3);
yr =1
while Qn>0
yindex = Gmhk*100
zindex = round(gs*100)
ts = (COG_Ton_Av ((zindex:yindex),:))
valuesforts(yr)=ts
yr = yr+1
end
I just posted parts of the while loop to make the question easier, I hope it is sufficient to answer the question.
While trying this i get following error:
Subscripted assignment dimension mismatch.
Error in cutoff_work14_priceescalation_and_stockpiling (line 286)
valuesforts(yr)=ts
The error means that ts is a different size to valuesforts (and it is indexed with yr as a vector.
If dimensions of TS vary on each iteration of the loop then use cell notation:
valuesforts = cell(<number of years>);
...
valuesforts{yr} = ts;
then the dimensions of ts won't matter.
To extract data also use { } e.g.
meanValues(yr) = mean(valuesforts{yr});
Bear in mind that the matrix within each cell of valuesforts will have same distentions as ts when it was assigned.
Alternatively, if TS is always the same size the pre-allocate valuesforts as:
valuesforts = zeros(<number of years>,<expected length of ts>,3);
...
valuesforts(yr,:,:) = ts;
Then depends on what you want to do with valuesforts.. reshape it or plot it.
In the worst case (not recommended), you can let the valuesforts grow with every loop iteration.
initialise with empty:
valuesforts=[];
then vertically append ts to valuesforts:
valuesforts = [valuesforts; ts];
this would give you a matrix with 3 columns and number of years * number of rows in ts in each loop iteration.
I have a matrix time-series data for 8 variables with about 2500 points (~10 years of mon-fri) and would like to calculate the mean, variance, skewness and kurtosis on a 'moving average' basis.
Lets say frames = [100 252 504 756] - I would like calculate the four functions above on over each of the (time-)frames, on a daily basis - so the return for day 300 in the case with 100 day-frame, would be [mean variance skewness kurtosis] from the period day201-day300 (100 days in total)... and so on.
I know this means I would get an array output, and the the first frame number of days would be NaNs, but I can't figure out the required indexing to get this done...
This is an interesting question because I think the optimal solution is different for the mean than it is for the other sample statistics.
I've provided a simulation example below that you can work through.
First, choose some arbitrary parameters and simulate some data:
%#Set some arbitrary parameters
T = 100; N = 5;
WindowLength = 10;
%#Simulate some data
X = randn(T, N);
For the mean, use filter to obtain a moving average:
MeanMA = filter(ones(1, WindowLength) / WindowLength, 1, X);
MeanMA(1:WindowLength-1, :) = nan;
I had originally thought to solve this problem using conv as follows:
MeanMA = nan(T, N);
for n = 1:N
MeanMA(WindowLength:T, n) = conv(X(:, n), ones(WindowLength, 1), 'valid');
end
MeanMA = (1/WindowLength) * MeanMA;
But as #PhilGoddard pointed out in the comments, the filter approach avoids the need for the loop.
Also note that I've chosen to make the dates in the output matrix correspond to the dates in X so in later work you can use the same subscripts for both. Thus, the first WindowLength-1 observations in MeanMA will be nan.
For the variance, I can't see how to use either filter or conv or even a running sum to make things more efficient, so instead I perform the calculation manually at each iteration:
VarianceMA = nan(T, N);
for t = WindowLength:T
VarianceMA(t, :) = var(X(t-WindowLength+1:t, :));
end
We could speed things up slightly by exploiting the fact that we have already calculated the mean moving average. Simply replace the within loop line in the above with:
VarianceMA(t, :) = (1/(WindowLength-1)) * sum((bsxfun(#minus, X(t-WindowLength+1:t, :), MeanMA(t, :))).^2);
However, I doubt this will make much difference.
If anyone else can see a clever way to use filter or conv to get the moving window variance I'd be very interested to see it.
I leave the case of skewness and kurtosis to the OP, since they are essentially just the same as the variance example, but with the appropriate function.
A final point: if you were converting the above into a general function, you could pass in an anonymous function as one of the arguments, then you would have a moving average routine that works for arbitrary choice of transformations.
Final, final point: For a sequence of window lengths, simply loop over the entire code block for each window length.
I have managed to produce a solution, which only uses basic functions within MATLAB and can also be expanded to include other functions, (for finance: e.g. a moving Sharpe Ratio, or a moving Sortino Ratio). The code below shows this and contains hopefully sufficient commentary.
I am using a time series of Hedge Fund data, with ca. 10 years worth of daily returns (which were checked to be stationary - not shown in the code). Unfortunately I haven't got the corresponding dates in the example so the x-axis in the plots would be 'no. of days'.
% start by importing the data you need - here it is a selection out of an
% excel spreadsheet
returnsHF = xlsread('HFRXIndices_Final.xlsx','EquityHedgeMarketNeutral','D1:D2742');
% two years to be used for the moving average. (250 business days in one year)
window = 500;
% create zero-matrices to fill with the MA values at each point in time.
mean_avg = zeros(length(returnsHF)-window,1);
st_dev = zeros(length(returnsHF)-window,1);
skew = zeros(length(returnsHF)-window,1);
kurt = zeros(length(returnsHF)-window,1);
% Now work through the time-series with each of the functions (one can add
% any other functions required), assinging the values to the zero-matrices
for count = window:length(returnsHF)
% This is the most tricky part of the script, the indexing in this section
% The TwoYearReturn is what is shifted along one period at a time with the
% for-loop.
TwoYearReturn = returnsHF(count-window+1:count);
mean_avg(count-window+1) = mean(TwoYearReturn);
st_dev(count-window+1) = std(TwoYearReturn);
skew(count-window+1) = skewness(TwoYearReturn);
kurt(count-window +1) = kurtosis(TwoYearReturn);
end
% Plot the MAs
subplot(4,1,1), plot(mean_avg)
title('2yr mean')
subplot(4,1,2), plot(st_dev)
title('2yr stdv')
subplot(4,1,3), plot(skew)
title('2yr skewness')
subplot(4,1,4), plot(kurt)
title('2yr kurtosis')
I am using interp1 to inteprolate some data:
temp = 4 + (30-4).*rand(365,10);
depth = 1:10;
dz = 0.5; %define new depth interval
bthD = min(depth):dz:max(depth); %new depth vector
for i = 1:length(temp);
i_temp(i,:) = interp1(depth,temp(i,:),bthD);
end
Here, I am increasing the resolution of my measurements by interpolating the measurements from 1 m increments to 0.5 m increments. This code works fine i.e. it gives me the matrix I was looking for. However, when I apply this to my actual data, it takes a long time to run, primarily as I am running an additional loop which runs through various cells. Is there a way of achieving what is described above without using the loop, in other words, is there a faster method?
Replace your for loop with:
i_temp = interp1(depth,temp',bthD)';
You can get rid of the transposes if you change the way that temp is defined, and if you are OK with i_temp being a 19x365 array instead of 365x19.
BTW, the documentation for interp1 is very clear that you can pass in an array as the second argument.
I have a homework problem, I think I did it correctly but need to make sure 100%. Can anyone check for me, before I hand it in?
Thank you.
Question:
Plot the function given by f (x) = 2 sin(2x) − 3 cos(x/2) over the in-
terval [0, 2π] using steps of length .001 (How?). Use the commands max and min to estimate the maximum and minimum points. Include the maximum and minimum points as tick marks on the x-axis and the maximum and minimum values as tick marks on the y-axis.
My code:
x=linspace(0,2*pi,6280);
f=#(x)...
2.*sin(2.*x)-3.*cos(x./2);
%f = #(x)2.*sin(2.*x)-3.*cos(x./2)
g=#(x)...
-1*(2.*sin(2.*x)-3.*cos(x./2));
%g = #(x)-1*(2.*sin(2.*x)-3.*cos(x./2))
[x3,y5]=fminbnd(g,0,2*pi);
%x3 = 4.0968
%y3 = -3.2647
[x2,y4]=fminbnd(f,0,2*pi);
%x2 =2.1864
%y2 = -3.2647
y2=max(f(x));
y3=min(f(x));
plot(x,f(x));
set(gca,'XTick',[x2 x3]);
set(gca,'YTick',[y2 y3]);
(*after I paste this code here, it appeared not as nice as I had it in my program, don't know why)
To create a vector with certain step do
x=0:0.001:2*pi;
Why do you have g(x) function and why are you using fminbind? Use MIN and MAX, return index of those values and find related x values.
[ymin, minindex] = min(f(x));
xmin = x(minindex);
For general case if you have multiple min/max values, index will contain only the first occurrence. Instead you can do:
minindex = find(y==ymin);
Or for real values to avoid precision error:
minindex = find(abs(y-ymin)<=eps);
Also your last statement returns error Values must be monotonically increasing. To avoid it sort your tick values.
set(gca,'XTick',sort([xmin xmax]));
set(gca,'YTick',sort([ymin ymax]));