I understand that you have a dataset containing radiation data and you want to group similar data and perform some analysis on top of it.
Since nothing is mentioned about the algorithm for grouping data, lets assume “k-means” clustering as the grouping algorithm. We can divide the entire task into three main chunks which are as follows:
- Loading and preparing data.
- Comparing and grouping time series by similarity.
- Identifying time series with highest and lowest values with low variability.
Kindly refer to the code below for better understanding:
% Load the data, part 1 of the task
load('DATA.mat');
dates = DATA(:, 1);
times = DATA(:, 2);
readings = DATA(:, 3);
dataTable = table(dates, times, readings);
% Get unique days
uniqueDays = unique(dates);
% Reshape data into matrix form where each row is a day
numDays = length(uniqueDays);
numTimePoints = sum(dates == uniqueDays(1));
dataMatrix = reshape(readings, numTimePoints, numDays)';
% Normalize data (optional, depending on your data characteristics)
dataMatrix = zscore(dataMatrix, 0, 2);
% Perform k-means clustering- grouping the data, part 2 of task
numClusters = 3;
[idx, ~] = kmeans(dataMatrix, numClusters);
% Create a grouping table
groupingTable = table(uniqueDays, idx, 'VariableNames', {'Day', 'Group'});
disp(groupingTable);
% Calculate mean and standard deviation for each day- part 3 of the task
means = mean(dataMatrix, 2);
stdDevs = std(dataMatrix, 0, 2);
% Define a threshold for low variability
threshold = 0.1;
lowVarIndices = stdDevs < threshold;
if any(lowVarIndices)
highMeanLowVar = uniqueDays(lowVarIndices & means == max(means(lowVarIndices)));
lowMeanLowVar = uniqueDays(lowVarIndices & means == min(means(lowVarIndices)));
else
highMeanLowVar = [];
lowMeanLowVar = [];
end
disp('Day with highest mean and low variability:');
disp(highMeanLowVar);
disp('Day with lowest mean and low variability:');
disp(lowMeanLowVar);
Kindly refer to the following documentation link for more information on “k-means” algorithm: https://www.mathworks.com/help/stats/kmeans.html
I hope this will help!
