<p>Currently, I found the right recipe for a time series regression problem to finally get acceptable to good results. </p>

<p>Here is the config file </p>

<pre><code>{
    "data": {
        "sequence_length":45,
        "train_test_split": 0.85,
        "normalise": false,
        "num_steps": 10
    },
    "training": {
        "epochs":30,
        "batch_size": 32
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "layers": [
            {
                "type": "lstm",
                "neurons": 161,
                "input_timesteps": 45,
                "input_dim": 161,
                "return_seq": true,
                "activation": "relu"
            },
            {
                "type": "dropout",
                "rate": 0.1
            },
            {
                "type": "lstm",
                "neurons": 161,
                "activation": "relu",
                "return_seq": false
            },
            {
                "type": "dense",
                "neurons": 128,
                "activation": "relu"
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}
</code></pre>

<p>Here is the results I got</p>

<p><a href="https://i.sstatic.net/K19Ki.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/K19Ki.png" alt="enter image description here"></a></p>

<p>What can be good improvements I can bring to my model so that I can get better results? There are a lot of small spikes and other places where the curve is rather constant that I should get a rise or fall of the curve.</p>

<neural-networks><recurrent-neural-networks>
Getting better results in improving the configuration
user1050421
# Getting better results in improving the configuration

(Asked by: username_0 on 2018-11-07)

Currently, I found the right recipe for a time series regression problem to finally get acceptable to good results. 
Here is the config file 
{
    "data": {
        "sequence_length":45,
        "train_test_split": 0.85,
        "normalise": false,
        "num_steps": 10
    },
    "training": {
        "epochs":30,
        "batch_size": 32
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "layers": [
            {
                "type": "lstm",
                "neurons": 161,
                "input_timesteps": 45,
                "input_dim": 161,
                "return_seq": true,
                "activation": "relu"
            },
            {
                "type": "dropout",
                "rate": 0.1
            },
            {
                "type": "lstm",
                "neurons": 161,
                "activation": "relu",
                "return_seq": false
            },
            {
                "type": "dense",
                "neurons": 128,
                "activation": "relu"
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}

Here is the results I got

What can be good improvements I can bring to my model so that I can get better results? There are a lot of small spikes and other places where the curve is rather constant that I should get a rise or fall of the curve.

**Tags:** neural-networks, recurrent-neural-networks


---

## 0 answers

