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TSER
Time-Series Engines
1. Overview
Time-Series Engines (TSER) process and validate temporal sequences of data such as sensor streams or periodic measurements.
TSER engines ensure temporal consistency before downstream computation.
2. Design Principles
-
Temporal Integrity
Time ordering and continuity are preserved. -
Signal Conditioning
Noise, gaps, and anomalies are handled explicitly. -
Non-Inferential
TSER engines condition data; they do not infer missing values.
3. Scope of Responsibility
3.1. What TSER Engines Do
- Filter noise and spikes
- Resample time-series data
- Validate temporal consistency
- Detect anomalies
4. What TSER Engines Do Not Do
- ❌ Estimate missing data (SEM)
- ❌ Aggregate across entities (AGGR)
- ❌ Compute ESG metrics (CALC)
5. Inputs
- Time-series datasets
- Temporal rules and thresholds
- Sampling or smoothing parameters
6. Outputs
- Conditioned time-series payloads
- Anomaly flags
- Provenance references
7. Canonical Identification
- Engine Type:
TSER - USO Code:
TSER - Category: Micro Engine (MICE)
Status: Stable
Owner: Computation Hub / MICE