Jira progress: loading…

TSYN

Time Series Synchronization Engines

1. Overview

Time Series Synchronization Engines (TSYN) are micro-engines within the ZAYAZ Computation Hub responsible for aligning, synchronizing, and deterministically interpolating time-series data.

TSYN engines ensure that temporal datasets with differing resolutions, calendars, or coverage can be safely combined or compared downstream.

TSYN is an engine category within the MICE taxonomy. Concrete implementations are registered as micro-engine instances (MEIDs) that implement the TSYN type.

2. Design Principles

1. Deterministic Interpolation
   Given the same inputs and rules, TSYN engines always produce the same output.

2. Explicit Temporal Rules
   All alignment and interpolation logic is rule-driven and versioned.

3. No Probabilistic Estimation
   TSYN engines do not infer missing values using statistical or AI models.

3. Scope of Responsibility

3.1. What TSYN Engines Do

• Align time-series to a common temporal grid
• Interpolate missing points using deterministic methods
• Synchronize datasets with differing frequencies
• Handle calendar alignment (e.g. fiscal vs calendar periods)
• Prepare time-series for aggregation or comparison

Typical use cases:

• Aligning hourly sensor data to monthly reporting periods
• Synchronizing facility-level time series before aggregation
• Harmonizing datasets from different reporting calendars

4. What TSYN Engines Do Not Do

• ❌ Probabilistic gap filling or estimation (SEM)
• ❌ Sensor-specific plausibility checks (SENS)
• ❌ Time-series filtering or anomaly detection (TSER)
• ❌ Computation of ESG metrics (CALC)

TSYN engines are temporal coordinators, not inferential engines.

5. Inputs

TSYN engines consume:

• One or more time-series datasets
• Target temporal resolution and alignment rules
• Explicit interpolation methods (e.g. linear, carry-forward)

Inputs may originate from:

• SENS outputs
• TSER outputs
• CALC or AGGR results requiring synchronization

6. Outputs

TSYN engines produce:

• Time-aligned and synchronized time-series payloads
• Interpolation metadata
• Provenance references to original time-series inputs

Outputs are commonly consumed by:

• AGGR
• NORM
• Reporting pipelines

7. Relationship to TSER and SEM

• TSER conditions and validates time-series signals
• TSYN aligns and synchronizes them
• SEM estimates missing values probabilistically

These categories may appear sequentially in a pipeline but serve distinct roles.

8. Audit & Provenance

Every TSYN execution records:

• Source time-series identifiers
• Alignment and interpolation rules
• Affected time windows
• Execution timestamps and engine version

This ensures replayability and auditability of synchronized data.

9. Canonical Identification

• Engine Type: TSYN
• USO Code: TSYN
• Category: Micro Engine (MICE-adjacent)
• Layer: Computation Hub

10. Design Rationale

By separating synchronization from conditioning and estimation, TSYN ensures that temporal alignment is explicit, deterministic, and auditable.

Time alignment is a semantic operation, not an implementation detail.

11. Related Engine Types

• SENS — Sensor Logic Units
• TSER — Time-Series Engines
• SEM — Stochastic Extrapolation Module
• AGGR — Aggregation Engines

---

Status: Stable
Owner: Computation Hub / MICE