<p>Yes, LSTM are ideal for this. For even stronger representational capacity, make your LSTM's multi-layered.
Using 1-dimensional convolutions in a CNN is a common way to exctract information from time series too, so there's no harm in trying. Typically, you'll test many models out and take the one that has best validation performance.</p>

CC BY-SA 4.0
2020-02-05T09:57:26.760Z
We have tried this models but the loss is extremely big (quintillion scale). What do you suggest to do further ?
estamos
CC BY-SA 4.0
2020-02-05T13:49:52.110Z
I added a new one , what do you think about it ? Whatever architecture I choose the training time is extremely big . Note that sequence length is 999 and I want to predict the 1000th .
estamos
CC BY-SA 4.0
2020-02-05T03:28:41.793Z
2020-02-05T03:28:41.793Z
Anon
<p>I have to build a neural network without any architecture limitations which have to predict the next value of a time series.</p>

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

<pre><code>0xbfb22b14
0xbfb22b10
0xbfb22b0c
0xbfb22b18
0xbfb22b14
</code></pre>

<p>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?</p>

<pre><code>def structure(step,n_features):
    # define model
    model = Sequential()
    model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(step, n_features)))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    return model
</code></pre>

<p>What about this one ?</p>

<pre><code>"model": {
        "loss": "mse",
        "optimizer": "adam",
    "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"
            }
</code></pre>

<neural-networks><deep-learning><prediction><time-series>
What's the best architecture for time series prediction with a long dataset?
estamos
# What's the best architecture for time series prediction with a long dataset?

(Asked by: username_0 on 2020-02-02)

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()
    model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(step, n_features)))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    return model

What about this one ?
"model": {
        "loss": "mse",
        "optimizer": "adam",
    "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"
            }

**Tags:** neural-networks, deep-learning, prediction, time-series


---

## 1 answers

### Answer by username_1 on 2020-02-05. Score: 1 | Accepted answer

Yes, LSTM are ideal for this. For even stronger representational capacity, make your LSTM's multi-layered.
Using 1-dimensional convolutions in a CNN is a common way to exctract information from time series too, so there's no harm in trying. Typically, you'll test many models out and take the one that has best validation performance.

> **Comments:**
> **username_0**: We have tried this models but the loss is extremely big (quintillion scale). What do you suggest to do further ?  
> **username_0**: I added a new one , what do you think about it ? Whatever architecture I choose the training time is extremely big . Note that sequence length is 999 and I want to predict the 1000th .  

---

