Time-Series-Papers

This is a repository for collecting papers and code in time series domain.

Table of Content

Linear
RNN and CNN
Transformer
GNN
LLM Framework
Diffusion Model
Benchmark and Dataset
Repositories

├─ Linear/  
  ├─ RNN and CNN/           
  ├─ Transformer/
  ├─ GNN/
  ├─ LLM Framework/
  ├─ Diffusion Model/
  ├─ Benchmark and Dataset/                      
  └─ Repositories/

Linear

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, Oreshkin et al., ICLR 2020. \[paper\]\[n-beats\]\[N-BEATS\]
DLinear: Are Transformers Effective for Time Series Forecasting, Zeng et al., AAAI 2023. \[paper\]\[code\]\[DiPE-Linear\]\[TimeLinear\]
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting, Ekambaram et al., KDD 2023. \[paper\]\[model\]\[example\]
FreTS: Frequency-domain MLPs are More Effective Learners in Time Series Forecasting, Yi et al., NeurIPS 2023. \[paper\]\[code\]\[FilterNet\]
Tiny Time Mixers (TTMs): Fast Pretrained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series, Ekambaram et al., arxiv 2024. \[paper\]\[code\]
FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting, Chen et al., arxiv 2023. \[paper\]\[code\]
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion, Han et al., NeurIPS 2024. \[paper\]\[code\]
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters, Lin et al., ICML 2024 Oral. \[paper\]\[code\]
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting, Wang et al., ICLR 2024. \[paper\]\[code\]
DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting, Qiu et al., KDD 2025. \[paper\]\[code\]
SearchCast: How Good Can Linear Models Be for Time-Series Forecasting, Huang et al., arxiv 2026. \[paper\]\[code\]

RNN and CNN

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks, Salinas et al., arxiv 2017. \[paper\]\[TimeSeries\]
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis, Wu et al., ICLR 2023. \[paper\]\[code\]\[slides\]
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks, Hou and Yu, arxiv 2024. \[paper\]\[code\]

Transformer

Transformers in Time Series: A Survey, Wen et al., IJCAI 2023. \[paper\]\[code\]
Deep Time Series Models: A Comprehensive Survey and Benchmark, Wang et al., arxiv 2024. \[paper\]\[code\]
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting, Zhou et al., AAAI 2021 Best paper. \[paper\]\[code\]
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting, Wu et al., NeurIPS 2021. \[paper\]\[code\]\[slides\]\[ETSformer\]
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, Xu et al., ICLR 2022. \[paper\]\[code\]\[slides\]\[TranAD\]
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting, Liu et al., NeurIPS 2022. \[paper\]\[code\]
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting, Liu et al., ICLR 2024 Spotlight. \[paper\]\[code\]
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting, Liu et al., ICLR 2022. \[paper\]\[code\]
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting, Zhou et al., ICML 2022. \[paper\]\[code\]\[DAMO-DI-ML\]
PatchTST: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers, Nie et al., ICLR 2023. \[paper\]\[code\]
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting, Zhang and Yan, ICLR 2023. \[paper\]\[code\]
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables, Wang et al., NeurIPS 2024. \[paper\]\[code\]\[code\]
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting, Liu et al., arxiv 2024. \[paper\]
MetaTST: Metadata Matters for Time Series: Informative Forecasting with Transformers, Dong et al., arxiv 2024. \[paper\]
Are Language Models Actually Useful for Time Series Forecasting, Tan et al., NeurIPS 2024. \[paper\]\[code\]\[CATS\]
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective, Wang et al., NeurIPS 2024. \[paper\]\[code\]\[ChatTime\]
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer, Zhang et al., NeurIPS 2024. \[paper\]\[code\]
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting, Chen et al., ICLR 2024. \[paper\]\[code\]
MASTER: Market-Guided Stock Transformer for Stock Price Forecasting, Li et al., AAAI 2024. \[paper\]\[code\]\[StockMixer\]\[Quant-Reading-List\]\[UMI KDD 2025\]

GNN

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, Jin et al., arxiv 2023. \[paper\]\[code\]
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks, Li et al., NeurIPS 2023. \[paper\]\[code\]
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective, Yi et al., NeurIPS 2023. \[paper\]\[code\]
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting, Cai et al., AAAI 2024. \[paper\]\[code\]

LLM Framework

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook, Jin et al., arxiv 2023. \[paper\]\[code\]
Large Language Models for Time Series: A Survey, Zhang et al., arxiv 2024. \[paper\]\[code\]\[Empowering-Time-Series-Analysis-with-LLM\]
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review, Su et al., arxiv 2024. \[paper\]

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling, Dong et al., NeurIPS 2023 Spotlight. \[paper\]\[code\]
One Fits All: Power General Time Series Analysis by Pretrained LM, Zhou et al., NeurIPS 2023 Spotlight. \[paper\]\[code\]\[AI-for-Time-Series-Papers-Tutorials-Surveys\]\[CALF\]
Large Language Models Are Zero-Shot Time Series Forecasters, Gruver et al., NeurIPS 2023. \[paper\]\[code\]
Lag-Llama: Towards Foundation Models for Time Series Forecasting, Rasul et al., arxiv 2023. \[paper\]\[code\]
TimesFM: A decoder-only foundation model for time-series forecasting, Das et al., ICML 2024. \[paper\]\[code\]
TimeGPT-1, Garza et al., arxiv 2023. \[paper\]\[nixtla\]\[sulie\]
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models, Jin et al., ICLR 2024. \[paper\]\[code\]
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models, Liu et al., NeurIPS 2024. \[paper\]\[code\]
Timer: Generative Pre-trained Transformers Are Large Time Series Models, Liu et al., ICML 2024. \[paper\]\[code\]\[Unified Time Series Dataset\]\[website\]\[slides\]\[OpenLTM\]
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting, Liu et al., arxiv 2024. \[paper\]\[code\]\[slides\]
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling, Dong et al., ICML2024. \[paper\]\[code\]\[slides\]
Sundial: A Family of Highly Capable Time Series Foundation Models, Liu et al., ICML 2025 Oral. \[paper\]\[code\]
MOMENT: A Family of Open Time-series Foundation Models, Goswami et al., ICML 2024. \[paper\]\[code\]
Unified Training of Universal Time Series Forecasting Transformers, Woo et al., ICML 2024. \[paper\]\[code\]
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning, Bian et al., arxiv 2024. \[paper\]
UNITS: A Unified Multi-Task Time Series Model, Gao et al., NeurIPS 2024. \[paper\]\[code\]
Chronos: Learning the Language of Time Series, Ansari et al., arxiv 2024. \[paper\]\[code\]
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables, Arango et al., arxiv 2025. \[paper\]\[code\]
Large language models can be zero-shot anomaly detectors for time series, Alnegheimish et al., arxiv 2024. \[paper\]
Foundation Models for Time Series Analysis: A Tutorial and Survey, Liang et al., arxiv 2024. \[paper\]\[granite-tsfm\]
Are Language Models Actually Useful for Time Series Forecasting?, Tan et al., arxiv 2024. \[paper\]\[code\]
LETS-C: Leveraging Language Embedding for Time Series Classification, Kaur et al., arxiv 2024. \[paper\]
Towards Neural Scaling Laws for Time Series Foundation Models, Yao et al., arxiv 2024. \[paper\]
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters, Chen et al., arxiv 2024. \[paper\]\[code\]
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts, Shi et al., ICLR 2025. \[paper\]\[code\]\[Moirai-MoE\]
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning, Xie et al., VLDB 2025. \[paper\]\[code\]
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting, Benechehab et al., arxiv 2025. \[paper\]\[code\]
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding, Zhang et al., arxiv 2025. \[paper\]
This Time is Different: An Observability Perspective on Time Series Foundation Models, Cohen et al., arxiv 2025. \[paper\]\[code\]
Time-R1: Towards Comprehensive Temporal Reasoning in LLMs, Liu et al., arxiv 2025. \[paper\]\[code\]\[Position\]
MIRA: Medical Time Series Foundation Model for Real-World Health Data, Li et al., arxiv 2025. \[paper\]\[code\]
Harnessing Vision-Language Models for Time Series Anomaly Detection, He et al., AAAI 2026 Oral. \[paper\]\[code\]\[ST-LLM\]
LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence, LimiX Team, arxiv 2025. \[paper\]\[code\]
Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling, Liu et al., arxiv 2026. \[paper\]
LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics, Ding et al., arxiv 2026. \[paper\]\[code\]

Diffusion Model

Diffusion-TS: Interpretable Diffusion for General Time Series Generation, Yuan and Qiao, ICLR 2024. \[paper\]\[code\]
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data, Yang et al., arxiv 2024. \[paper\]\[code\]
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model, Cao et al., arxiv 2024. \[paper\]
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation, Wang et al., ICML 2025. \[paper\]\[code\]
UTSD: Unified Time Series Diffusion Model, Ma et al., arxiv 2024. \[paper\]
Auto-Regressive Moving Diffusion Models for Time Series Forecasting, Gao et al., AAAI 2025. \[paper\]\[code\]

Benchmark and Dataset

TSPP: A Unified Benchmarking Tool for Time-series Forecasting, Bączek et al., arxiv 2023. \[paper\]\[code\]
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods, Qiu et al., arxiv 2024. \[paper\]\[code\]
A Survey of Generative Techniques for Spatial-Temporal Data Mining, Zhang et al., arxiv 2024. \[paper\]
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis, Liu et al., arxiv 2024. \[paper\]\[code\]\[MM-TSFlib\]
GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation, Aksu et al., arxiv 2024. \[paper\]\[code\]
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting, Hu et al., arxiv 2025. \[paper\]\[code\]
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks, Qiao et al., arxiv 2026. \[paper\]