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tsLSTMx
View on CRAN: Click
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Download and install tsLSTMx package within the R console
Install from CRAN:
install.packages("tsLSTMx")
Install from Github:
library("remotes")
install_github("cran/tsLSTMx")
Install by package version:
library("remotes")
install_version("tsLSTMx", "0.1.0")
Attach the package and use:
library("tsLSTMx")
Maintained by
Sandip Garai
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-01-12
Latest Update: 2024-01-12
Description:
It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically designed to handle time series with an exogenous variable, allowing users to denote whether data was available for a particular period or not. The package encompasses various functionalities, including hyperparameter tuning, custom loss function support, model evaluation, and one-step-ahead forecasting. With an emphasis on ease of use and flexibility, it empowers users to explore, evaluate, and deploy LSTM models for accurate time series predictions and forecasting in diverse applications. More details can be found in Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
How to cite:
Sandip Garai (2024). tsLSTMx: Predict Time Series Using LSTM Model Including Exogenous Variable to Denote Zero Values. R package version 0.1.0, https://cran.r-project.org/web/packages/tsLSTMx
Previous versions and publish date:
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imports, suggests or enhances
Functions, R codes and Examples using
the tsLSTMx R package
Some associated functions: best_model_on_validation . check_and_format_data . compare_predicted_vs_actual . convert_to_numeric_matrices . convert_to_tensors . define_early_stopping . embed_columns . forecast_best_model . initialize_tensorflow . predict_y_values . reshape_for_lstm . split_data . ts_lstm_x_tuning .
Some associated R codes: compiled_code_tslstmx.R . Full tsLSTMx package functions and examples
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