What you are trying to do here is forecast the future values of a time series. This is a predictive problem and the future values will depend on a number of latent factors. I will assume all we have access to is historical data from the series as your question indicates.
If you want to predict a future value for the time series, you should not only use the current value as an input, but rather you should use a chunk of the historical data. Since you have 18,000,000 instances, this is a lot, you can make your network quite complex in order to capture some latent trends hidden inside your data which can help predict the future value. To predict a value at time $t$ we will use the $k$ previous values. This hyper-parameter needs to be effectively tuned.
Restructure the data
We will structure the data such that the features $X$ are the $k$ previous time measurements, and the output target $Y$ is the current time measurement. The one that is being estimated by the model.
k = 3
X, Y = , 
for i in range(len(col1) - k):
X = np.asarray(X)
Y = np.asarray(Y)
Split your data
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)
Using the data in a Keras model
This is a simple Keras model which should work as a first iteration step. However, due to the small amount of data you provided us I cannot get any meaningful results after training.
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv1D, MaxPooling1D, Reshape
from keras.callbacks import ModelCheckpoint
from keras.models import model_from_json
from keras import backend as K
x_train = x_train.reshape(len(x_train), k, )
x_test = x_test.reshape(len(x_test), k, )
input_shape = (k,)
model = Sequential()
epochs = 10
batch_size = 128
# Fit the model weights.
history = model.fit(x_train, y_train,