Stock price prediction using a Temporal Fusion Transformer

For sequence-to-sequence beginners. PyTorch-implemented 1DCNN, LSTM, Attention, and Transformers.

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.

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

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

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

A deep learning-based application for predicting future stock prices using Temporal Fusion Transformer (TFT) and global market data.

Time-series prediction project for a logistics company

Sales Forecasting using Temporal Fusion Transformers

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

Trying the Temporal Fusion Transformer model for forecasting Renewable energy.

Transformer-based neural network (inspired by the Temporal Fusion Transformer - TFT) for Kraken

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

Final project for the Time Series course of the MSc degree in Data Science and Artificial Intelligence (DSAI), University of Trieste

Implementation of financial market regime identification models including traditional statistical approaches and deep learning methods (GRSTU), featuring a novel application of Temporal Fusion Transformers to regime classification.

Blockchain-enabled federated learning system for smart building Digital Twins using Hyperledger Fabric and Temporal Fusion Transformers. Privacy-preserving IoT sensor prediction across 76 rooms with decentralized AI training.