Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

• 类型：arxiv
• 标识：2609.06008
• 链接：https://arxiv.org/abs/2609.06008
• 主分类：engineering
• 形态：method
• TLDR：We present Cadence, an error-bounded lossy compressor for numeric time series pairing a 330M-parameter time-series foundation model (Google TimesFM-3) with an adaptive arithmetic coder, guaranteeing |x_t-x_t|leτ on every sample. One negative result constrains the design space: for lossless coding a foundation model is worth nothing, because bits saved are logarithmic in predictor accuracy, Δb=log_2(MAE_{old}/MAE_{new}). So the 1.51times advantage TimesFM-3 holds over a 32-tap linear predictor buys 0.60 bits of 20.28, a median gain of +0.03%. Error-bounded coding escapes this at one point: once
• 待LLM分类：是
• 标题中文：Cadence：基于时序基础模型的需求时序误差有界有损压缩
• TLDR中文：我们提出 Cadence，一种面向数值时序的误差有界有损压缩器，将 3.3 亿参数的时序基础模型（Google TimesFM-3）与自适应算术编码器相结合，保证每个样本满足 |x_t - x̂_t| ≤ τ。一项负面结论限定了设计空间：在无损编码场景下，基础模型毫无价值，因为节省的比特数仅与预测器精度呈对数关系 Δb = log_2(MAE_old / MAE_new)。因此 TimesFM-3 相对 32 阶线性预测器 1.51 倍的精度优势，在 20.28 比特中仅换取 0.60 比特，中位数增益仅 +0.03%。误差有界编码仅在一点上突破了这一限制。
• 来源文件：
• /inbox/tom/_candidates/2026-09-09-agent-rag-longcontext-candidates.json