Scalable and user friendly neural 🧠 forecasting algorithms.

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

DeepMIMO python dataset codes for mmWave and massive MIMO

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the …

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

A Library for Advanced Deep Time Series Models.

Repository of Transformer based PyTorch Time Series Models

"Self-Supervised Contrastive Forecasting", accepted at International Conference on Learning Representations (ICLR) 2024

Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"

The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

This is the official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting".

The formal Implement of our work@Nature portfolio journal Communications Engineering

An extension of LightGBM to probabilistic modelling and prediction

Normalizing flows in PyTorch

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759