Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

Deep Adaptive Input Normalization for Time Series Forecasting

Multivariate Time Series Transformer, public version

Code for our NeurIPS 2019 paper "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models"

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

Stock prediction thru TFT

time series analysis models source code

proof of concept for a transformer-based time series prediction model

LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Notes for first year Computing at Imperial College London

🎓 Path to a free self-taught education in Computer Science!

🎓 Path to a free self-taught education in Computer Science!

Master programming by recreating your favorite technologies from scratch.

Plasma protocol compatible ETH Exchange Platform

Financial modeling in R