How to use the same minmaxscaler used on the training data with new data? Im using the keras LSTM model to make prediction, and the code above is to scale the data:
inputs are shaped like  (n, 11, 1) and the label is 1D 
DailyDemand.py

#scaling data
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),11 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)

# Split train and test data
x_train=x[0: train_end ,]
x_test=x[train_end +1: ,]
y_train=y[0: train_end]
y_test=y[train_end +1:] 
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
# Train and save the Model named fit1 in a json and h5 files 
[....]
# serialize model to JSON
model_json = fit1.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
fit1.save_weights("model.h5")
print("&gt;&gt;&gt;&gt; Model saved to model.h5 in the disk")


and Now im trying to predict a new values of a new data with this trained model. so i loaded the model from the files:

predict.py

from DailyDemand import scaler_y
from DailyDemand import scaler_x
[...]
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")

########################################
# make prediction with the loaded model

FeaturesTest = [267,61200,695,677,70600,116700,130200,768,659,741,419300]
xaa = np.array(FeaturesTest).reshape ((1,11 )).astype(float)
print(xaa)
xaa = scaler_x.fit_transform(xaa) 
xaa = xaa.reshape(xaa.shape +(1,))
print("print FeaturesTest scalled: ")
print(xaa) # incorrect scalled value, always returns -1 ones 



xaa = [[[-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]
  [-1.]]]



tomorrowDemand = loaded_model.predict(xaa)
print("tomorrowDemand scalled: ", tomorrowDemand)
prediction = scaler_y.inverse_transform(np.array(tomorrowDemand).reshape ((len(tomorrowDemand), 1))).astype(int)
print ("the real demand is 95900 and the prediction is: ", prediction)


The problem is how i can use the same scaler used in the training on the new data?
i want to know if i made a mistake in this code to use the same scaller on the new data?
You are refitting scaler_x on your test set, which you don't want.  Change this line:

xaa = scaler_x.fit_transform(xaa)


to

xaa = scaler_x.transform(xaa)


You are getting [-1, -1, ..., -1] because with one sample, each feature is equal to the minimum.