Stock price prediction using a Temporal Fusion Transformer

Sequence-to-sequence model implementations including RNN, CNN, Attention, and Transformers using PyTorch

Using Temporal Fusion Transformer for Book sales forecasting use case. We use the model implementation available in Pytorch Forecasting library.

New product demand forecasting via Content based learning for multi-branch stores: Ali and Nino Use Case

This repository is the implementation of the paper: ViT2 - Pre-training Vision Transformers for Visual Times Series Forecasting. ViT2 is a framework designed to address generalization & transfer learning limitations of Time-Series-based forecasting models by encoding the time-series to images using GAF and a modified ViT architecture.

This project is a time series forecasting model using the Temporal Fusion Transformer (TFT) deep learning architecture. The model is trained and evaluated on the M4 competition dataset, achieving state-of-the-art results in multi-step forecasting tasks.

Predicted Spanish day-ahead energy demand and price with 97.5% accuracy using a range of ML and statistical time series forecasting models including XGBoost, Transformers, TFTs and SARIMA.

Interpreting County-Level COVID-19 Infections using Transformer and Deep Learning Time Series Models

Time-series prediction project for a logistics company

Forecasting daily total sales 🧾 of different gifting items 🎁 using holiday data 🎄, promotional sales data 🏷️ , and other time-series features 🕛.

Temporal Fusion Transformer model實作，目的為熟悉特徵工程、建模流程及預測結果視覺化，並深入研究模型架構與內部邏輯，強化對新模型的理解能力。當前仍在優化中

A plug and play framework for Temporal Fusion Transformer. Predict your future!

Devday2023 - Optimizer Power Use - Forecasting power generation and power demand at grid

Trying the Temporal Fusion Transformer model for forecasting Renewable energy.

Sales Forecasting using Temporal Fusion Transformers

End-to-end Deep Learning (TFT) demand forecasting system for Retail/FMCG with automated MLOps pipeline on Google Cloud (Vertex AI) for inventory optimization. Demonstrates advanced time series modeling, feature engineering, explainability (SHAP), and scalable deployment.