This project implements a deep learning model called the Temporal Fusion Transformer (TFT) for time series forecasting. The TFT model is a powerful architecture that combines the strengths of both transformers and RNNs to capture long-term dependencies and seasonal patterns in time series data. This project includes data preprocessing, model training, and hyperparameter tuning steps, and provides a comprehensive description on how to use the TFT model for time series forecasting.
It is my 8th sem Science Engineering and Technology Project. Feel free to check it out!
Link to the publication: https://link.springer.com/chapter/10.1007/978-981-97-3245-6_3