About LSTM model error modifying

% Import Excel data from outside
file_path = '한화 합본.xlsx'; %Excel file path specified
sheet_name = 'Sheet1'; name % sheet
data = readtable(file_path, 'Sheet', sheet_name);

% Specify selected variables
selectedVariables = {'E_Price', 'ror', 'Construction', 'Vacancy', 'KOSPI', 'Bond', 'CC2', 'Exchange', 'Rent'};

% Extract only the selected variables from the data frame
selectedData = data(:, selectedVariables);

% Pre-processing of training data
XTrain = cell(1, size(selectedData, 2));
TTrain = cell(1, size(selectedData, 2));
for i = 1:size(selectedData, 2)
    X = selectedData.(selectedVariables{i});
    XTrain{i} = (X(1:end-1) - mean(X(1:end-1))) / std(X(1:end-1));
    TTrain{i} = (X(2:end) - mean(X(2:end))) / std(X(2:end));
end

% Defining LSTM Neural Network Architecture
numSelectedChannels = size(selectedData, 2);
layers = [

    sequenceInputLayer (1, 'Name', 'input') % change

Invalid expression. When calling a function or indexing a variable, use parentheses. Otherwise, check for mismatched delimiters.

    lstmLayer(128, 'Name', 'lstm')
    fullyConnectedLayer(numSelectedChannels, 'Name', 'fc')
    regressionLayer('Name', 'output')];

% Setting training options
options = trainingOptions("adam", ...
    'MaxEpochs', 200, ...
    'SequencePaddingDirection', 'left', ...
    'Shuffle', 'every-epoch', ...
    'Plots', 'training-progress', ...
    'Verbose', 0);

% LSTM Neural Network Training
net = trainNetwork(XTrain, TTrain, layers, options);

% Keep the rest of the code the same



% LSTM Neural Network Test
XTest = XTrain; % Simple use of training data as test data
YTest = predict(net, XTest, 'SequencePaddingDirection', 'left');

% RMSE calculation
rmse = zeros(1, size(YTest, 1));
for i = 1:size(YTest, 1)
    rmse(i) = sqrt(mean((YTest{i} - TTrain{i}).^2, 'all'));
end

% Visualize the RMSE histogram
figure
histogram(rmse)
xlabel("RMSE")
ylabel("Frequency")

% Calculate mean RMSE
meanRMSE = mean(rmse);

% Visualize prediction results (open loop prediction)
figure
tiledlayout(numSelectedChannels, 1)
title("Open Loop Forecasting")

for i = 1:numSelectedChannels
    nexttile
    plot(TTrain{i})
    hold on
    plot(1:size(YTest{i}, 2), YTest{i}, '--')
    ylabel(selectedVariables{i})
end

xlabel("Time Step")
nexttile(1)
legend(["Input" "Forecasted"])

Based on the attached time series data, we want to create a one-year yield prediction model after the deadline for collecting data through the LSTM model... Errors continue to occur

[Walter Roberson]

% Import Excel data from outside
file_path = '한화 합본.xlsx'; %Excel file path specified
sheet_name = 'Sheet1'; sheet_name % sheet

sheet_name = 'Sheet1'

data = readtable(file_path, 'Sheet', sheet_name);

% Specify selected variables
selectedVariables = {'E_Price', 'ror', 'Construction', 'Vacancy', 'KOSPI', 'Bond', 'CC2', 'Exchange', 'Rent'};

% Extract only the selected variables from the data frame
selectedData = data(:, selectedVariables);

% Pre-processing of training data
XTrain = cell(1, size(selectedData, 2));
TTrain = cell(1, size(selectedData, 2));
for i = 1:size(selectedData, 2)
    X = selectedData.(selectedVariables{i});
    XTrain{i} = (X(1:end-1) - mean(X(1:end-1))) / std(X(1:end-1));
    TTrain{i} = (X(2:end) - mean(X(2:end))) / std(X(2:end));
end

% Defining LSTM Neural Network Architecture
numSelectedChannels = size(selectedData, 2);
layers = [
        sequenceInputLayer(1, 'Name', 'input') % change
    lstmLayer(128, 'Name', 'lstm')
    fullyConnectedLayer(numSelectedChannels, 'Name', 'fc')
    regressionLayer('Name', 'output')];

% Setting training options
options = trainingOptions("adam", ...
    'MaxEpochs', 200, ...
    'SequencePaddingDirection', 'left', ...
    'Shuffle', 'every-epoch', ...
    'Plots', 'training-progress', ...
    'Verbose', 0);

% LSTM Neural Network Training
net = trainNetwork(XTrain, TTrain, layers, options);

Error using trainNetwork
The training sequences are of feature dimension 304 but the input layer expects sequences of feature dimension 1.

% Keep the rest of the code the same



% LSTM Neural Network Test
XTest = XTrain; % Simple use of training data as test data
YTest = predict(net, XTest, 'SequencePaddingDirection', 'left');

% RMSE calculation
rmse = zeros(1, size(YTest, 1));
for i = 1:size(YTest, 1)
    rmse(i) = sqrt(mean((YTest{i} - TTrain{i}).^2, 'all'));
end

% Visualize the RMSE histogram
figure
histogram(rmse)
xlabel("RMSE")
ylabel("Frequency")

% Calculate mean RMSE
meanRMSE = mean(rmse);

% Visualize prediction results (open loop prediction)
figure
tiledlayout(numSelectedChannels, 1)
title("Open Loop Forecasting")

for i = 1:numSelectedChannels
    nexttile
    plot(TTrain{i})
    hold on
    plot(1:size(YTest{i}, 2), YTest{i}, '--')
    ylabel(selectedVariables{i})
end

xlabel("Time Step")
nexttile(1)
legend(["Input" "Forecasted"])