TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

arXiv:2609.29792 · cs.CL, cs.AI, cs.CE · Submitted 2026-09-24 · Read on arXiv

cs.CL, cs.AI, cs.CE

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Alignment — 46× in this paper · explained in 1 episode
• Foundation Model — 40× in this paper · explained in 3 episodes
• Foundation Models — 30× in this paper · explained in 7 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Time Series Forecasting — 10× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Trace — 7× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Context-Aided Forecasting — 3× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Probabilistic Forecasting — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Two-stage training — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• Toward Native Multimodal Modeling: A Roadmap
• Chronos: Learning the Language of Time Series
• TimeSeriesExam: A time series understanding exam
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction
• A decoder-only foundation model for time-series forecasting
• Emerging Properties in Unified Multimodal Pretraining
• TimeGPT-1
• MOMENT: A Family of Open Time-series Foundation Models
• TimeOmni-VL: Unified Models for Time Series Understanding and Generation
• Measuring Massive Multitask Language Understanding
• LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data
• Dynin-Omni: Omnimodal Unified Large Diffusion Language Model
• OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
• Sundial: A Family of Highly Capable Time Series Foundation Models
• Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
• Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
• Chameleon: Mixed-Modal Early-Fusion Foundation Models

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