# What's the best architecture for time series prediction with a long dataset?

I have to build a neural network without any architecture limitations which have to predict the next value of a time series.

The dataset is composed of 400.000 values, which are given in hex format. For example

0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14


I think LSTM is suitable for this problem, but I am worried about the length of the input. Would it be a good idea to use CNN?

def structure(step,n_features):
# define model
model = Sequential()
return model


"model": {
"loss": "mse",
"save_dir": "saved_models",
"layers": [
{
"type": "lstm",
"neurons": 999,
"input_timesteps": 998,
"input_dim": 1,
"return_seq": true
},
{
"type": "dropout",
"rate": 0.05
},
{
"type": "lstm",
"neurons": 100,
"return_seq": false
},
{
"type": "dropout",
"rate": 0.05
},
{
"type": "dense",
"neurons": 1,
"activation": "linear"
}