S_P_500_Close

The S&amp;P 500's closing price as data provides an excellent real-world application.
The S&amp;P 500 index, a key benchmark for U.S. stock market performance, is vital for
portfolio management, risk assessment, and market analysis. The original data frame
contains 1340 rows and 2 variables. Here, we have taken only 200 rows and 2 variables
for demonstration.

datasets

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 

S_P_500_Close function

A data frame with 200 rows and 2 variables:<dl>
<dt>Date</dt>
<dd>Formatted date</dd><dt>Price</dt>
<dd>Numerical price values</dd>
</dl>

Format

S&amp;P 500's closing price data — S_P_500_Close

A data frame with 200 rows and 2 variables:<dl>
<dt>Date</dt>
<dd>Formatted date</dd>

<dt>Price</dt>
<dd>Numerical price values</dd>


</dl>

S&amp;P 500's closing price data

S_P_500_Close: S&P 500's closing price data

Description

Usage

Arguments

Format