How can i predict the future values which are not in the data-frame. The following code predicts the values on the training data-set and validation data-set but not the future values. I want to predict next 30 days, by looking at past 100 days.How can i do This. Thanks

df2 = df.reset_index()['High']
scaler = MinMaxScaler()
df2 = scaler.fit_transform(np.array(df2).reshape(-1, 1))

train_size = int(len(df2)*0.65)
test_size = len(df2) - train_size
train_data,test_data = df2[0:train_size,:],df2[train_size:len(df2),:1]


def create_dataset(dataset, time_step = 1):
    dataX,dataY = [],[]
    for i in range(len(dataset)-time_step-1):
                   a = dataset[i:(i+time_step),0]
                   dataX.append(a)
                   dataY.append(dataset[i + time_step,0])
    return np.array(dataX),np.array(dataY)

time_step = 100
X_train,Y_train =  create_dataset(train_data,time_step)
X_test,Y_test =  create_dataset(test_data,time_step)

model = Sequential()
model.add(LSTM(50,return_sequences = True,input_shape = (X_train.shape[1],1)))
model.add(LSTM(50,return_sequences = True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss = 'mean_squared_error',optimizer = 'adam')
model.fit(X_train,Y_train,validation_data = (X_test,Y_test),epochs = 100,batch_size = 64 ,verbose = 1)

train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

train_predict = scaler.inverse_transform(train_predict)
test_predict = scaler.inverse_transform(test_predict)
Y_test = scaler.inverse_transform(np.array(Y_test).reshape(-1, 1))

look_back = 100
trainPredictPlot = np.empty_like(df2)
trainPredictPlot[:,:] = np.nan
trainPredictPlot[look_back : len(train_predict)+look_back,:] = train_predict

testPredictPlot = np.empty_like(df2)
testPredictPlot[:,:] = np.nan
testPredictPlot[len(train_predict)+(look_back)*2 + 1 : len(df2) - 1,:] = test_predict

plt.plot(scaler.inverse_transform(df2))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show() here

I’m trying to predict the next 30 days at once by looking 100 days backward. I tried few sample code but they were old and deprecated.