install_r_dependencies

Installs Python dependencies (TensorFlow, Keras, Pandas) in a Conda environment for the transformerForecasting package.

Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.

G H Harish Nayak

transformerForecasting

Transformer Deep Learning Model for Time Series Forecasting

Md Wasi Alam 

B Samuel Naik 

G Avinash 

Kabilan S

Varshini B S 

Mrinmoy Ray 

Rajeev Ranjan Kumar 

install_r_dependencies function

<dl><dt>env_name</dt>
<dd>Character string specifying the Conda environment name (default: "r-reticulate").</dd></dl>

Arguments

Install Package Dependencies — install_r_dependencies

<dl>

<dt>env_name</dt>
<dd>Character string specifying the Conda environment name (default: "r-reticulate").</dd>

</dl>

install_r_dependencies: Install Package Dependencies

Description

Usage

Value

Arguments

Examples