LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems.
Bhandari, H., Rimal, B., Pokhrel, N., Rimal, R., & Dahal, K. (2022). LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling[Formula presented]. Software Impacts, 14 https://doi.org/10.1016/j.simpa.2022.100396