arXiv Machine Learning
Sep 18
When Does Retrieval Help Time-Series Forecasting?
arXiv:2609. 20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry.
Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model
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When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
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CADENCE: A Confidence-Adaptive Dual-Expert Network for Fast and Accurate Time Series Classification
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arXiv Machine Learning
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Trend strength predicts when generative foundation models win: a power-controlled benchmark, a mechanism, and an actionable selection rule
arXiv:2607. 19383v1 Announce Type: cross Abstract: Pretrained generative foundation models cast forecasting as conditional generation from a learned predictive distribution and forecast unseen series zero-shot.
arXiv Machine Learning
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Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
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arXiv AI
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When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers
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arXiv Machine Learning
Jul 15
When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecasting
arXiv:2607. 12248v1 Announce Type: cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets.
arXiv Machine Learning
Sep 22
One Patch, Three Roles: What Is Actually Coupled in Autoregressive Time-Series Forecasting?
arXiv:2609.23686v1 Announce Type: new Abstract: Patch-based autoregressive time-series forecasting often ties input representation, learned transitions, and recursive execution to one patch length. W...
arXiv AI
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Winning by Peeking: Unenforced Budgets and Test-Set Selection Inflate Short-Budget AutoML Comparisons
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arXiv AI
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Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency
arXiv:2607. 07207v1 Announce Type: cross Abstract: We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.
arXiv AI
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Fine-Tuning a 7B Advisor on Free-Tier GPUs: An Adapter-Handoff Recipe and a Synthetic-Data Reliability Caution
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