tspartition

Identify the observations in the training sets and test sets of a tspartition object for expanding window cross-validation.

Use 20 time-dependent observations to create three training sets and three test sets. Specify a gap of two observations between each training set and its corresponding test set.

c = tspartition(20,"ExpandingWindow",3, ...
    GapSize=2);

Find the training set indices for the three windows. A value of 1 (true) indicates that the corresponding observation is in the training set for that window.

trainWindow1 = training(c,1);
trainWindow2 = training(c,2);
trainWindow3 = training(c,3);

Find the test set indices for the three windows. A value of 1 (true) indicates that the corresponding observation is in the test set for that window.

testWindow1 = test(c,1);
testWindow2 = test(c,2);
testWindow3 = test(c,3);

Combine the training and test set indices into one matrix where a value of 1 indicates a training observation and a value of 2 indicates a test observation.

data = [trainWindow1 + 2*testWindow1, ...
    trainWindow2 + 2*testWindow2, ...
    trainWindow3 + 2*testWindow3];

Visualize the different sets by using a heat map.

colormap = lines(3);
heatmap(double(data),ColorbarVisible="off", ...
    Colormap=colormap);
xlabel("Window")
ylabel("Observation")
title("Expanding Window Cross-Validation Scheme")

For each window, the observations in red (with a value of 1) are in the training set, the observations in yellow (with a value of 2) are in the test set, and the observations in blue (with a value of 0) are ignored. For example, observation 11 is a test observation in window one, a gap observation in window two, and a training observation in window three.

Identify the observations in the training sets and test sets of a tspartition object for sliding window cross-validation.

Use 20 time-dependent observations to create five training sets and five test sets.

c = tspartition(20,"SlidingWindow",5);

Find the training set indices for the five windows. A value of 1 (true) indicates that the corresponding observation is in the training set for that window.

trainWindows = zeros(c.NumObservations,c.NumTestSets);
for i = 1:c.NumTestSets
    trainWindows(:,i) = training(c,i);
end

Find the test set indices for the five windows. A value of 1 (true) indicates that the corresponding observation is in the test set for that window.

testWindows = zeros(c.NumObservations,c.NumTestSets);
for i = 1:c.NumTestSets
    testWindows(:,i) = test(c,i);
end

Combine the training and test set indices into one matrix where a value of 1 indicates a training observation and a value of 2 indicates a test observation.

data = trainWindows + 2*testWindows;

Visualize the different sets by using a heat map.

colormap = lines(3);
heatmap(double(data),ColorbarVisible="off", ...
    Colormap=colormap);
xlabel("Window")
ylabel("Observation")
title("Sliding Window Cross-Validation Scheme")

For each window, the observations in red (with a value of 1) are in the training set, the observations in yellow (with a value of 2) are in the test set, and the observations in blue (with a value of 0) are ignored. For example, observations 9 through 11 are test observations in window two and training observations in window three. Because of the default values for the training set size, test set size, step size, and direction for creating sliding windows, tspartition does not use some of the oldest observations (1 and 2) in any window.

Identify the observations in the training set and test set of a tspartition object for holdout validation.

Use 25% of 20 time-dependent observations to create a test set. The corresponding training set contains the remaining observations.

c = tspartition(20,"Holdout",0.25);

Find the test set indices.

testIndices = test(c);

Visualize the two sets of observations by using a heat map.

h = heatmap(double(testIndices),ColorbarVisible="off");
h.XDisplayLabels = "";
ylabel("Observation")
title("Holdout Validation Scheme")

The observations in light blue (with a value of 0) are in the training set, and the observations in dark blue (with a value of 1) are in the test set. In a holdout validation scheme for time series data, the latest observations (in this case, observations 16 through 20) are in the test set.