A regression model fails at predicting high and low values.
I'm training a regression model where the input and output data has same dimension (energy). The input consists on previous values of the energy for a given time and the output is a value of the energy in the future.
I'm scaling both the input and the output using standard sampler (I tried also min-max and robust scaler).
I'm using around 1.5k samples for training, and predicting the value on 40k samples (after seeing that there's not that a big of an increase in accuracy when training with more data). I'm using MAE to score the model, and I get around 300.
For models I'm using support vector machine and neural network (with 2 layers, 100 neurons each) and both exhibit same behaviour.
This is the plot I obtain when I compare the real and predicted values. As you can see, the model fails at predicting high and low values, and I don't know what to try.
I made sure the input contains samples with low and high values.