# LSTM : Bad Data or Low preparation

I have three features {feature1, feature2, feature3} the middle have negative sign for some values and i am trying to predict the middle one using LSTM with window size of 10 but i get very high RMS and printing the expected vs predicted doesn't show good values, the difference is really high. The below is a histogram for the three features and boxplot.

note that i tried to normalize the data as below

scaler1 = MinMaxScaler(feature_range=(0, 1))
df['NormFeature1'] = scaler1.fit_transform(df[["Feature1"]])
scaler2 = MinMaxScaler(feature_range=(-1, 1))
df['NormFeature2'] = scaler2.fit_transform(df[["Feature2"]])
scaler3 = MinMaxScaler(feature_range=(0, 1))
df['NormFeature3'] = scaler3.fit_transform(df[["Feature3"]])


I also tried to remove the outliers using the below code, but didn't get better, also you can check the histogram for after removing the outliers.
The below code is repeated for the three features before the MinMaxScalar just replaced the index in line 2 with the index of the feature

data = np.asarray(df)
normalized_data = preprocessing.StandardScaler().fit_transform(data[:,[index]])
outliers_rows, outliers_columns = np.where(np.abs(normalized_data)>3)
df = df.drop(df.index[outliers_rows])


And here is another look for the Histograms

Couldn't add new histogram here because of the max number of links

example of the predicted data

>Expected=-10.0, Predicted=-0.7
>Expected=6.0, Predicted=-0.2
>Expected=-6.0, Predicted=-1.1
>Expected=-10.0, Predicted=-0.1
>Expected=40.0, Predicted=0.4
>Expected=-5.0, Predicted=-3.2
>Expected=0.0, Predicted=-2.6
>Expected=15.0, Predicted=-1.7
>Expected=-13.0, Predicted=-3.0
>Expected=-3.0, Predicted=-2.2
>Expected=-5.0, Predicted=0.3
>Expected=-39.0, Predicted=-0.6
>Expected=3.0, Predicted=3.3
>Expected=13.0, Predicted=1.4
>Expected=-11.0, Predicted=-1.5
>Expected=20.0, Predicted=-0.9
>Expected=13.0, Predicted=-1.8
>Expected=4.0, Predicted=-3.7
>Expected=13.0, Predicted=-2.9
>Expected=9.0, Predicted=-3.0
>Expected=-8.0, Predicted=-3.7
>Expected=-8.0, Predicted=-1.9
>Expected=7.0, Predicted=-0.5
>Expected=-11.0, Predicted=-1.7
>Expected=17.0, Predicted=-0.5
>Expected=-21.0, Predicted=-2.2


So is there a missing point, should i have applied some more preprocessing on the data ? or does the data not good for LSTM, i don't think there is other way for sequence to sequence regression ?