# Getting better results in improving the configuration

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",
"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.

Try using an RMSProp optimizer instead of Adam optimizer. Also try decreasing the batch size and keep a small learning rate like 0.001.

• This is for a time-series forecasting problem, so yes, LSTM is useful here. Nov 7 '18 at 1:58