How can I solve mini-batch size issue in my LSTM network?

Hi,currently I am trying to develop an weather prediction neural network, My plan is to let the system take the 12 hours historical data to forecast 1 hour in the future. Thus, I used LSTM to predict the weather but there is one issue that keep bothering me, My LSTM keep complaining about the mini-batch size and I fail to understand the issue at here. Furthermore, I want to know how to let LSTM to take 12 hours historical data to forecast one hour(I assume time step is the key at here so I set the time step as 12 but I am not certain). The data is already provided.Thanks in advance!.

I already tried this solution but the mini-batch size issue still appear:

https://www.mathworks.com/matlabcentral/answers/504175-multi-step-ahead-forecasting-with-lstm

Here's the code snippet:

%Read the table
data = readtable('hourly_data.csv');

%extract the hourly data in 2016
data_2016 = data(65761:74544, :);
%plot each features
stackedplot(data_2016, {'tempC', 'windspeedKmph', 'humidity', 'cloudcover','precipMM'})

trainingset = data_2016(:,{'tempC', 'windspeedKmph', 'humidity', 'cloudcover','precipMM'});
numTimeStepsTrain = floor(0.8*height(trainingset));
dataTrain = trainingset(1:numTimeStepsTrain,:);
dataTest = trainingset(numTimeStepsTrain+1:end,:);

XTrain = dataTrain(1:end-1,1:4);
YTrain = dataTrain(2:end,5);
XTest = dataTest(1:end-1,1:4);
YTest = dataTest(2:end,5);


XTrain = table2array(XTrain);
YTrain = table2array(YTrain);
XTest = table2array(XTest);
YTest = table2array(YTest);

mu = mean(XTrain);
sig = std(XTrain);

XTrain = (XTrain - mu) / sig;
YTrain = (YTrain - mu) / sig;

XTest = (XTest - mu) / sig;
YTest = (YTest - mu) / sig;

[r,c] = size(XTrain);
[m,n] = size(XTest);
Xcell = cell(r,1);
for i = 1:r
    Xcell{i} = transpose(XTrain(i,1:end));
end
Ycell = cell(r,1);
for i = 1:r
    Ycell{i} = YTrain(i,1:end);
end
XTestcell = cell(m,1);
for i = 1:1756
    XTestcell{i} = XTest(i,1:end);
end
YTestcell = cell(m,1);
for i = 1:1756
    YTestcell{i} = YTest(i,1:end);
end

numFeatures = 1;
numResponses = 1;
numHiddenUnits = 50;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits)
    fullyConnectedLayer(numResponses)
    regressionLayer];

options = trainingOptions('adam', ...
    'MaxEpochs',10, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'MiniBatchSize',12,...
    'LearnRateDropPeriod',125, ...
    'LearnRateDropFactor',0.2, ...
    'Verbose',0, ...
    'Plots','training-progress');

net = trainNetwork(Xcell,Ycell,layers,options);

YPred = [];
net = predictAndUpdateState(net,Xcell);
stepsAhead = 12; % you can use 1,2,3,4 on any value of steps ahead
for i = 2:stepsAhead+1
    [net,YPred(:,i)] = predictAndUpdateState(net,XTestcell(:,i-1),"SequenceLength",114);
end

Answer 1

[Rohit Pappu]

A plausible solution would be to

• Change the MiniBatch SIze to 114
• Convert Xcell, Ycell, and XTestCell to double type

Xcell = [Xcell{:}];
Ycell = [Ycell{:}];
XTestcell = [XTestcell{:}];
YPred = [];
net = predictAndUpdateStatey(net,Xcell);
stepsAhead = 12; % you can use 1,2,3,4 on any value of steps ahead
for i = 2:stepsAhead+1
    [net,YPred(:,i)] = predictAndUpdateState(net,XTestcell(:,i-1),"SequenceLength",114,"MiniBatchSize",114);
end