Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model (Google TimesFM 3)
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Updated
Sep 11, 2026 - Python
Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model (Google TimesFM 3)
A pure-MLX Swift port of Google Research's TimesFM-3, a 330M-parameter foundation model for time-series forecasting. It runs natively on Apple Silicon with no PyTorch dependency in the Swift package, and matches the companion Python/MLX port to ~1e-6.
Strumento per l’analisi e la previsione del SuperEnalotto che verifica l’indipendenza delle estrazioni, valida i metodi predittivi con backtesting walk-forward e confronta le previsioni di TimesFM 3.0 con il caso.
Privacy-preserving, locally-hosted multimodal AI compliance copilot for banking AML/SAR - RAG over regulations, zero-shot forecasting, and a QLoRA-tuned SAR writer. Retrieval Recall@4 0.955, anomaly F1 0.938.
Native .NET inference for the Kronos K-line and TimesFM 3.0 foundation model times series forecasting.
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