Abstract— GPU or VGA (graphic processing unit) is a vital
component of computers and laptops, used for tasks such as
rendering videos, creating game environments, and compiling
large amounts of code. The price of GPU/VGA has fluctuated
significantly since the start of the COVID-19 pandemic in 2020,
due in part to the increased demand for GPUs for remote work
and online activities. Furthermore, accurate GPU price
forecasting can have broader implications beyond the computer
hardware industry, with potential applications in investment
decision-making, production planning, and pricing strategies for
manufacturers. This research aims to forecast future GPU prices
using deep learning-based time series forecasting using the
Transformer model. We use daily prices of NVIDIA RTX 3090
Founder Edition as a test case. We use historical GPU prices to
forecast 8, 16, and 30 days. Moreover, Transformer we compare
the results of the Transformer model with two other models, RNN
and LSTM. We found that to forecast 30 days; the Transformer
model gets a higher coefficient of correlation (CC) of 0.8743, a
lower root mean squared error (RMSE) value of 34.68, and a lower
mean absolute percentage error (MAPE) of 0.82 compared to the
RNN and LSTM model. These results suggest that the model is an
effective and efficient method for predicting GPU prices.