  Machine and deep learning-based algorithms are the emerging approaches in
addressing prediction problems in time series. These techniques have been shown
to produce more accurate results than conventional regression-based modeling.
It has been reported that artificial Recurrent Neural Networks (RNN) with
memory, such as Long Short-Term Memory (LSTM), are superior compared to
Autoregressive Integrated Moving Average (ARIMA) with a large margin. The
LSTM-based models incorporate additional "gates" for the purpose of memorizing
longer sequences of input data. The major question is that whether the gates
incorporated in the LSTM architecture already offers a good prediction and
whether additional training of data would be necessary to further improve the
prediction.
  Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the
input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research
question of interest is then whether BiLSTM, with additional training
capability, outperforms regular unidirectional LSTM. This paper reports a
behavioral analysis and comparison of BiLSTM and LSTM models. The objective is
to explore to what extend additional layers of training of data would be
beneficial to tune the involved parameters. The results show that additional
training of data and thus BiLSTM-based modeling offers better predictions than
regular LSTM-based models. More specifically, it was observed that BiLSTM
models provide better predictions compared to ARIMA and LSTM models. It was
also observed that BiLSTM models reach the equilibrium much slower than
LSTM-based models.
