# why is MSE of prediction way different from loss over batches

I am new to machine learning so forgive me if i ask stupid question. I have a time series data and i split it into training and test set.

This is my code:

from numpy import array
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# split a univariate sequence into samples
def split_sequence(sequence, n_steps_in, n_steps_out):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps_in
out_end_ix = end_ix + n_steps_out
# check if we are beyond the sequence
if out_end_ix > len(sequence):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)

# choose a number of time steps
n_steps_in, n_steps_out = 10, 5
# split into samples
X, y = split_sequence(trainlist, n_steps_in, n_steps_out)
# define model
model = Sequential()
# fit model
history = model.fit(X, y, epochs=2000, verbose=0)
# demonstrate prediction
x_input = array(testlist[0:10])
x_input = x_input.reshape((1, n_steps_in))
yhat = model.predict(x_input, verbose=0)
yhat=list(yhat[0])


when i do print(history.history['loss'][-10:-1]) it gives me roughly 0.55 and when i do

from sklearn.metrics import mean_squared_error
mean_squared_error(testlist[11:16],yhat)


it gives me 0.11. Why is it so different?