LSTM multiple feature regression data preparation - time-series

Question

I am modeling an LSTM model that contains multiple features and one target value. It is a regression problem.
I have doubts that my data preparation for the LSTM is erroneous; mainly because the model learns nothing but the average of the target value.
The following code I wrote is for preparing the data for the LSTM:
# df is a pandas data frame that contains the feature columns (f1 to f5) and the target value named 'target'
# all columns of the df are time series data (including the 'target')
# seq_length is the sequence length
def prepare_data_multiple_feature(df):
X = []
y = []
for x in range(len(df)):
start_id = x
end_id = x + seq_length
one_data_point = []
if end_id + 1 <= len(df):
# prepare X
for col in ['f1', 'f2', 'f3', 'f4', 'f5']:
one_data_point.append(np.array(df[col].values[start_id:end_id]))
X.append(np.array(one_data_point))
# prepare y
y.append(np.array(df['target'].values[end_id ]))
assert len(y) == len(X)
return X, y
Then, I reshape the data as follows:
X, y = prepare_data_multiple_feature(df)
X = X.reshape((len(X), seq_length, 5)) #5 is the number of features, i.e., f1 to f5
is my data preparation method and data reshaping correct?

Answer 1

As #isp-zax mentioned, please provide a reprex so we could reproduce the outcome and see where the problem lies.
As an aside, you could use for col in df.columns instead of listing all the column names and (minor optimisation) the first loop should be executed for x in range(len(df) - seq_length), otherwise at the end you execute the loop seq_length - 1 many times without actually processing any data. Also, df.values[a, b] will not include the element at index b so if you want to include the "window" with last row inside your X the end_id can be equal to the len(df), i.e. you could execute your inner condition (prepare and append) for if end_id <= len(df):
Apart from that I think it would be simpler to read if you sliced the dataframe across columns and rows at the same time, without using one_data_point, i.e.
to select seq_length rows without the (last) target column, simply do:
df.values[start_id, end_id, :-1]