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Abstract: This paper proposes a novel framework for stock price forecasting that integrates visual representations of time series data with an advanced deep learning model, named Temporal Fusion Transformer (TFT). Specifically, historical Open, High, Low, and Close (OHLC) prices of Apple stock from 2015 to 2025 are transformed into images using Gramian Angular Fields (GAF), from which high-level features are extracted via a pre-trained ResNet-18 network to serve as the past covariates of the TFT model. To evaluate the performance, we compare the TFT model against a Long Short-Term Memory (LSTM) network trained on raw OHLC data. Empirical evaluations reveal that the TFT model achieves an RMSE of 5.05, and MAPE of 1.99 %, significantly outperforming a baseline LSTM model with corresponding values of 3 8. 0 0, and 16.21, highlighting its potential for improved accuracy in financial time series prediction. These findings highlight the effectiveness of integrating imagebased time series representations with deep learning models for financial forecasting, offering a promising avenue for improving predictive accuracy in stock price forecasting.