Universal Redundancies in Time Series Foundation Models
cs.LG, stat.ML
Submitted: 2026-02-02
Updated: 2026-08-30
Code: https://github.com/abao1999/tsfm-lens
Terminology
Sources
- Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting
- GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
- Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs
- This Time is Different: An Observability Perspective on Time Series Foundation Models
- Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
- Constructing Efficient Fact-Storing MLPs for Transformers
- TimeGPT-1
- How Do LLMs Use Their Depth?
- Language Models Implement Simple Word2Vec-style Vector Arithmetic
- Self-Attention Attribution: Interpreting Information Interactions Inside Transformer
- How much do language models memorize?
- Understanding Factual Recall in Transformers via Associative Memories
- Panda: A pretrained forecast model for chaotic dynamics
- Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
- Test-time regression: a unifying framework for designing sequence models with associative memory
- When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models
- Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
- Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
- AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction
- Zero-shot forecasting of chaotic systems
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