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Cadence uses foundation model for error-bounded time series compression

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Cadence, a novel error-bounded lossy compression system for time series data. Cadence integrates Google's TimesFM-3 foundation model with an adaptive arithmetic coder to ensure sample-level accuracy guarantees. The system demonstrates significant improvements over classical predictors on energy demand and transit ridership data, outperforming existing methods by considerable margins. AI

IMPACT This approach could significantly improve data storage and transmission efficiency for time series applications, particularly in energy and transportation sectors.

RANK_REASON The cluster describes a research paper detailing a new method for time series compression using a foundation model.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Cadence uses foundation model for error-bounded time series compression

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The cluster describes a research paper detailing a new method for time series compression using a foundation model.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Roberto Tacconelli ·

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

    arXiv:2609.06008v1 Announce Type: cross Abstract: 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 $|\hat{x}_t-x_t|\le\tau$ on eve…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Cadence pairs a time-series foundation model with an adaptive arithmetic coder for error-bounded lossy compression, achieving substantial gains over classical predictors while reporting negative results on lossless coding and cross-batch determinism.

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