Body: <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>
  
Tags: <neural-networks><recurrent-neural-networks>
Title: Getting better results in improving the configuration
UserDisplayName: user1050421
ThreadText: # 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
  
  