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Tempus is a high accuracy time-series analysis project

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Tempus is a high accuracy time-series analysis project

Tempus is a project aimed at high accuracy modeling of time series data or regression problems. It implements an improved kernel regression algorithm based on the support vector machine theory by professors Vapnik, Chervonenkis and Lerner but with several improvements such are nested kernels, multilayered weights, massive parallelization and systemic parameter tuning. Tempus provides several signal decomposition methods and automated feature engineering. This new implementation of SVM allows for using of any statistical model, even itself, as a kernel function. This is done by calculating the ideal kernel matrix which is used as a reference for measuring the kernel function fitness. So far LightGBM, Torch, Path, RBF and Global alignment kernels have been implemented. Tempus achieves significantly higher accuracy than other competing models even on the most complex data.

Visit productView on Hacker NewsOct 21, 2025
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nativeThis product was originally launched on this platform.
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