Before revision:  The proposed methodology is able to consistently outperform the baseline LSTM model, and it achieves competitive results on benchmarking datasets , in particular outperforming all other methods on the CIF2016  dataset.
After revision:  The proposed methodology is able to consistently outperform the baseline LSTM model, and it achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 dataset.