cwang-nus

LTSF-Linear

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

UniTime

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting (WWW 2024)

SIESTA

PyTorch implementation of the SIESTA algorithm from our TMLR-2023 paper "SIESTA: Efficient Online Continual Learning with Sleep"

DAC-ML

This is the code for the paper DAC-ML accepted by ICDM21.

UCTB

An Open Source Spatio-Temporal Prediction Package

PDFormer

[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.

STAEformer

[CIKM'23] Official code for our paper "Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting".

DL-Traff-Graph

[CIKM 2021 Resource Paper] DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction (Graph Part)

TransGTR

Open-source code of TransGTR.

ST-SSL

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction

ttab

[ICML23] On Pitfalls of Test-Time Adaptation

USSFC-Net

awesome-test-time-adaptation

Collection of awesome test-time (domain/batch/instance) adaptation methods

awesome-source-free-test-time-adaptation

A curated list of papers in Test-time Adaptation, Test-time Training and Source-free Domain Adaptation

NeurIPS2023-One-Fits-All

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

Best-Incremental-Learning

An Incremental Learning, Continual Learning, and Life-Long Learning Repository

Awesome-Incremental-Learning

Awesome Incremental Learning

LargeST

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting (NeurIPS 2023 DB Track)

Z-GCNETs

mamba

Mamba SSM architecture

Awesome-state-space-models

Collection of papers on state-space models

SRD

Official pytorch implementation of Spatial Relation Decomposition method (AAAI 23)

awesome-public-datasets

A topic-centric list of HQ open datasets.

STC-Dropout

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

FreTS

Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting"

TSFpaper

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 type of model.

Koopa

Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803

transformers

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

Urban_Concept_Drift

[CIKM 2023] MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

tsf-new-paper-taste

A code implementation of new papers in the time series forecasting field.