Awesome Transformer Forecasters

Key papers

• LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters (2023)
  Ching Chang et al.
  14.28
• Chronos-2: From Univariate to Universal Forecasting (2025)
  Abdul Fatir Ansari et al.
  11.41
• Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting (2025)
  Siru Zhong et al.
  10.54
• LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization (2025)
  Wenzhe Niu et al.
  8.67
• ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables (2025)
  Sebastian Pineda Arango et al.
  7.95
• Looped World Models (2026)
  Hongyuan Adam Lu et al.
  7.37
• SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting (2024)
  Xiongxiao Xu et al.
  7.08
• Panda: A pretrained forecast model for chaotic dynamics (2025)
  Jeffrey Lai et al.
  6.58
• FlowState: Sampling Rate Invariant Time Series Forecasting (2025)
  Lars Graf et al.
  6.51
• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts (2024)
  Xiaoming Shi et al.
  6.36
• EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting (2025)
  Wei Li et al.
  6.20
• Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs (2025)
  Taibiao Zhao et al.
  6.01
• Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks (2024)
  Zijian Zhang et al.
  5.91
• Transformer Model for Alzheimer's Disease Progression Prediction Using Longitudinal Visit Sequences (2025)
  Mahdi Moghaddami et al.
  5.87
• FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting (2025)
  Wenzhen Yue and Yong Liu and Xianghua Ying and Bowei Xing and Ruohao Guo and Ji Shi
  5.84
• Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model (2025)
  Wei Wang et al.
  5.76
• Cross-Modal Temporal Fusion for Financial Market Forecasting (2025)
  Yunhua Pei and John Cartlidge and Anandadeep Mandal and Daniel Gold and Enrique Marcilio and Riccardo Mazzon
  5.70
• Positional Encoding in Transformer-Based Time Series Models: A Survey (2025)
  Habib Irani and Vangelis Metsis
  5.59
• Using Pre-trained LLMs for Multivariate Time Series Forecasting (2025)
  Malcolm L. Wolff et al.
  5.54
• TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding (2025)
  Haochuan Zhang et al.
  5.54
• CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting (2025)
  Kuan Lu et al.
  5.54
• Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting (2024)
  Chu Myaet Thwal et al.
  5.51
• Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation (2025)
  Chenxi Liu et al.
  5.40
• ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting (2025)
  Hyunwoo Seo and Chiehyeon Lim
  5.10
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting (2024)
  Yong Liu et al.
  5.02
• Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation (2025)
  C\'ecile Rousseau et al.
  4.98
• A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting (2025)
  Tingting Diao et al.
  4.87
• CITRAS: Covariate-Informed Transformer for Time Series Forecasting (2025)
  Yosuke Yamaguchi et al.
  4.87
• TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data (2025)
  Ege Onur Taga et al.
  4.82
• Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting (2025)
  Yuchen Luo et al.
  4.80
• Unveiling the Potential of Text in High-Dimensional Time Series Forecasting (2025)
  Xin Zhou and Weiqing Wang and Shilin Qu and Zhiqiang Zhang and Christoph Bergmeir
  4.76
• Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching (2025)
  Meng Wang et al.
  4.76
• Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting (2025)
  Jingru Fei et al.
  4.76
• Selective Learning for Deep Time Series Forecasting (2025)
  Yisong Fu et al.
  4.75
• Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts (2024)
  Aaron Van Poecke et al.
  4.71
• T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion (2025)
  Abdul Monaf Chowdhury et al.
  4.64
• CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning (2024)
  Peiyuan Liu et al.
  4.63
• CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting (2025)
  Minhyuk Lee et al.
  4.47
• Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering (2025)
  Yiming Niu et al.
  4.47
• Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay (2025)
  Hussain Jagirdar et al.
  4.42
• Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare (2025)
  Shahriar Noroozizadeh et al.
  4.42
• Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization (2025)
  Peiliang Gong et al.
  4.42
• MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications (2025)
  Ning Xin et al.
  4.42
• TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching (2026)
  Zhe Li et al.
  4.39
• FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance (2026)
  Jiaze Sun et al.
  4.39
• PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks (2026)
  Jichao Li et al.
  4.39
• Does Normalization Choice Matter for Causal Large Time-Series Models? (2026)
  Samy-Melwan Vilhes (LMAC) et al.
  4.39
• One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data (2026)
  Amrijit Biswas et al.
  4.39
• CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting (2026)
  Yosuke Yamaguchi et al.
  4.39
• Models Take Notes at Prefill: KV Cache Can Be Editable and Composable (2026)
  Bojie Li
  4.39
• The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance (2026)
  Csaba Kiss et al.
  4.39
• Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators (2026)
  Yuji Takubo et al.
  4.39
• The Discrete-Log Clock: How a Transformer Learns Modular Multiplication (2026)
  Huu Danh Nguyen (Stanford University)
  4.39
• Discrete Autoregressive Transformer for Generative Mechanism Synthesis (2026)
  Anar Nurizada et al.
  4.39
• MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense (2026)
  Abhishek Bhardwaj et al.
  4.39
• Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining (2026)
  Dong Hyeon Mok et al.
  4.39
• Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines (2026)
  Gram Koski et al.
  4.39
• TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins (2026)
  Yuxiang Luo et al.
  4.39
• ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics (2026)
  Kexin Wu et al.
  4.39
• Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models (2026)
  Vansh Bansal
  4.39