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_Arsenie_Boca_ t1_irr9bb4 wrote

Reply to comment by Constant-Cranberry29 in how to find out the problem when want to do testing the model? by Constant-Cranberry29

No this is not a modelling issue. It actually isnt a real issue at all. Predicting a very long trajectory is simply very hard. At each timestep, a slight error will occur which will exponentiate, even if the error per timestep is marginal. Imagine being asked to predict a certain stock price. Given some expertise and current information, you might be able to do it for tomorrow, but can you do it precisely for the next year?

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Constant-Cranberry29 OP t1_irra20x wrote

then is there any suggestion for me so that the model I have made can predict properly?

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_Arsenie_Boca_ t1_irrcmwh wrote

If all you want to see is the two curves close to each other, I guess you could size up the model, so that it overfits terribly. But is that really desirable?

If my assumption that you predict the whole graph autoregressively is correct, then I believe it works just fine. You should check the forecast horizon and think about what it is you want to achieve in the end

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Constant-Cranberry29 OP t1_irreagz wrote

Do you mean I need size up in this part?

def ResNet50Regression():

`Res_input = layers.Input(shape=(178,))`

# 128

`width = 128`



`x = dens_block(Res_input,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = dens_block(x,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = dens_block(x,width)`

`x = identity_block(x,width)`

`x = identity_block(x,width)`



`x = layers.BatchNormalization()(x)`

`x = layers.Dense(1,activation='linear')(x)`

`model = models.Model(inputs=Res_input, outputs=x)`



`return model`
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