LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling

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 forecastin...

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Bibliographic Details
Published inSoftware impacts Vol. 14; p. 100396
Main Authors Bhandari, Hum Nath, Rimal, Binod, Pokhrel, Nawa Raj, Rimal, Ramchandra, Dahal, Keshab R.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2022
Subjects
Data science
Deep learning
LSTM
Prediction
Sequential data modeling
Time series
Deep learning
Data science
LSTM
Sequential data modeling
Prediction
Time series
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Summary: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.
ISSN:2665-9638
2665-9638
DOI:10.1016/j.simpa.2022.100396