R-universe - ronit10976 (Ronit10976)

TSdeeplearning - Deep Learning Model for Time Series Forecasting

Provides deep learning models for time series forecasting using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). <doi:10.1007/s00521-021-06621-3>.

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1.48 score 1 dependents 583 downloads

EEMDlstm - EEMD Based LSTM Model for Time Series Forecasting

Forecasting univariate time series with ensemble empirical mode decomposition (EEMD) with long short-term memory (LSTM). For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.

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1.00 score 547 downloads

stlARIMA - STL Decomposition and ARIMA Hybrid Forecasting Model

Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

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1.00 score 178 downloads