Implements a Random Walk (RW)-informed Long Short-Term Memory
(LSTM) network for univariate time series forecasting. The LSTM is
trained on the full series with a composite loss that blends the usual
mean squared error (MSE) between predictions and observations with the
MSE between predictions and the fitted Random Walk, weighted by a
parameter lambda (0 gives a standard LSTM, 1 gives a fully RW-guided
LSTM). The Random Walk, either simple or with drift, is selected
automatically by its in-sample root mean squared error (RMSE), and the
lambda value with the lowest training RMSE is retained. The LSTM
hyperparameters can be supplied manually or tuned by grid search, and
functions for out-of-sample forecasting, accuracy measures and plots
are provided. The methodology is motivated by hybrid forecasting
framework proposed by Yeasin and Paul (2024)