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Plato provides fast approximate analytics on time series, by precomputing andstoring compressed time series. Plato's key novelty is the delivery of tightdeterministic error guarantees for time series analytics. Plato evaluates anytime series expression composed by the linear algebra operators over vectors,along with arithmetic operators. This large scope of possible expressionsincludes common use cases such as correlation and cross-correlationexpressions. Each time series is segmented either by fixed-length segmentationor by (a usually more effective) variable-length segmentation. Each segment iscompressed by an estimation/compression function that approximates the actualvalues and is coming from a user-chosen function family, as taught by manyprior works. The novelty is that Plato associates to each segment 1 to 3(depending on the case) precomputed error measures and, using them, Platocomputes tight deterministic error guarantees for analytics over thecompressions. Importantly, some compression families lead to much betterdeterministic error guarantees. This work identifies two broad estimationfunction family groups (Vector Space (VS) and Linear Scalable Family (LSF)),which lead to theoretically and practically high-quality guarantees, even forexpressions (eg correlation) that combine multiple time series that have beenindependently compressed and may, thus, use misaligned segmentations. Thetheoretical aspect of "high quality" is crisply captured by the AmplitudeIndependence (AI) property: An AI guarantee does not depend on the amplitude ofthe involved time series, even when we combine multiple time series. Theexperiments on four real-life datasets showed that when the novel AI guaranteeswere applicable, the approximate query results were certified to be very close(typically 1%) to the true results.

Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees