why predict value is too low

main.py

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from numpy import newaxis

import matplotlib.pyplot as plt
import numpy as np
from keras.layers.core import Dense, Activation, Dropout
from sklearn.model_selection import train_test_split


import time


def nomalization(arr):
    result = []
    pivot = arr[0]
    result.append(1)
    for elem in arr[1:]:
        result.append(elem / pivot)
    result = np.array(result)
    result = result[..., newaxis]
    return result

data = np.genfromtxt('./data/삼성전자.csv')
data = np.array(nomalization(data))
days = np.array(range(0, len(data)))
data = np.array([[a] for a in data])
days = np.array([[a] for a in days])

print(data.shape)
print(days.shape)
X_train, X_test, Y_train, Y_test = train_test_split(data, days, test_size=0.3, random_state=0)

model = Sequential()

model.add(LSTM(
    input_dim=1,
    output_dim=50,
    return_sequences=True))
model.add(Dropout(0.2))

model.add(LSTM(
    100,
    return_sequences=False))
model.add(Dropout(0.2))

model.add(Dense(
    output_dim=1))
model.add(Activation('linear'))

start = time.time()
model.compile(loss='mse', optimizer='rmsprop')
print('compilation time : ', time.time() - start)

model.fit(
    X_train,
    Y_train,
    batch_size=1024,
    nb_epoch=5,
    validation_split=0.05)

def predict_sequences_multiple(model, data, window_size, prediction_len):
    return model.predict(data)


predictions = predict_sequences_multiple(model, X_test, 30, 30)
for i in range(len(predictions)):
    print("true: %s" % (X_test[i]))
    print("prediction: %s" % (predictions[i]))
    print("delta: %s" % (X_test[i] - predictions[i]))