Simulation Temporal Data Module (STDM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Input & Data Standard (SIDS)
Operational Layer: Simulation Input & Data Governance Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Simulation Temporal Data
Version: 1.0
Status: Canonical · Open Module
Effective Date: 18 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · CLIA® · MGIA™ · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodological Intellectual Property
Canonical Language: English (UCL™)
Governed Space
Simulation Temporal Data
Canonical Definition
Simulation Temporal Data Module (STDM) defines the governance frameworkthrough which the temporal properties of Simulation Inputs are identified,
aligned, bounded, assessed, and preserved so that information entering a
simulation remains temporally appropriate for the reality period, sequence,
frequency, duration, synchronization, and dynamic behavior the simulation is
intended to represent.
STDM establishes the temporal-input layer of SIDS by governing whether data is
not merely available and technically usable, but temporally fit for the
simulation context in which it is relied upon.
It governs the transition:
Simulation-Usable Input → Temporal Context → Temporal Alignment → Temporal
Fitness Assessment → Temporal Conditions & Limitations → Temporally Governed
Simulation Input
Operational Role
STDM serves as the fourth internal governance space of the Simulation Input &Data Standard (SIDS).
The preceding modules establish:
SIIM — What information is entering the simulation?
SDRM — What reality can that information legitimately represent?
SDTM — What happened to that information before simulation use?
STDM asks:
Does the information represent the correct time, temporal sequence, frequency,
duration, synchronization, and dynamic context required by the simulation?
The governed progression is:
Simulation Input → Temporal Identity → Temporal Reference → Temporal Alignment
→ Temporal Coverage → Temporal Gap & Distortion Assessment → Temporal Fitness
Status → Simulation Use
Module Function
STDM exists to prevent: old data being treated as current without assessment,current data being assumed relevant to historical conditions, observations
from different periods being combined as if simultaneous, temporal order being
lost during preprocessing, sampling frequency hiding fast system behavior,
short observation periods being generalized to long-term dynamics, seasonal
data being generalized across the entire year, temporal gaps disappearing
during interpolation, asynchronous sensors appearing synchronized, delayed
information being treated as real-time information, historical relationships
being assumed stable after regime change, future extrapolation being
represented as observation, and simulations producing precise outputs from
temporally inappropriate input foundations.
Temporal Identity
Every material temporal Simulation Input should preserve sufficientinformation to determine when the represented information applies.
Temporal identity may include: timestamp, observation period, collection
period, effective period, duration, sequence position, sampling interval,
temporal resolution, or another relevant temporal reference.
Temporal Reference
A Temporal Reference establishes the time basis against which an input isinterpreted.
Examples include:
UTC
Local Time
Mission Time
System Runtime
Relative Simulation Time
Event TimeHistorical Period
or another defined reference system.Where multiple references are used, their relationship should remain explicit.
Observation Time
Observation Time identifies when the represented phenomenon actually occurredor was observed.
This may differ from:
Collection Time
Processing Time
Transmission Time
or:Simulation Consumption Time.
These distinctions may materially affect dynamic simulations.
Event Time vs Processing Time
SIMULOS® establishes:When information was processed is not necessarily when the represented event
occurred.
For real-time and event-driven simulations, confusing these temporal
properties may distort causal or sequential relationships.
Temporal Currency
Temporal Currency asks:Is the input sufficiently current for the simulation inquiry?
Currency is context-dependent.
Data from ten years ago may be suitable for one simulation and materially
obsolete for another.
No Universal Freshness Rule
SIMULOS® establishes:Temporal fitness cannot be determined from data age alone.
The relevant question is whether material properties of the represented
reality have changed since the data was produced.
Temporal Applicability
An input may be temporally applicable only to a defined period.For example:
Applicable: 2024–2026 operating
configuration
does not automatically establish:Applicable: 2030 projected configuration.
Temporal applicability should therefore remain bounded.
Temporal Alignment
Temporal Alignment establishes whether multiple Simulation Inputs refer tosufficiently compatible time periods or temporal states.
Conceptually:
Input A Time Basis + Input B Time Basis + Simulation Time Basis → Temporal
Alignment Assessment Cross-Source Temporal Alignment
Different sources may contain observations from different periods.
For example:
Traffic Data — 2025
Road Infrastructure Data — 2022
Vehicle Population Data — 2026
Combining them may be legitimate, but the temporal differences should beassessed where they can materially affect simulation behavior.
Temporal Synchronization
Synchronization governs whether inputs that interact dynamically aresufficiently aligned in time.
This is particularly important for: sensor simulations, robotics, autonomous
systems, control systems, industrial systems, communication systems, financial
simulations, and real-time digital twins.
Synchronization Tolerance
Perfect synchronization may not always be required.A simulation may define an acceptable temporal tolerance.
For example:
Maximum Sensor Alignment Difference: ±20
ms
The tolerance should reflect the dynamics of the simulated system rather thantechnical convenience alone.
Temporal Sequence
Some simulation behavior depends upon the order of events.Conceptually:
A → B → C
may produce a materially different outcome from:
B → A → C
STDM therefore preserves sequence where sequence affects simulated causality
or behavior.
No Sequence Neutrality Principle
SIMULOS® establishes:Temporal order must not be treated as irrelevant where changing event order
can materially change the simulated outcome.
Temporal Resolution
Temporal Resolution defines the smallest meaningful time interval representedby an input.
Examples include: milliseconds, seconds, minutes, hours, days, months, or
years.
The required resolution depends upon the dynamics being simulated.
Resolution Fitness
A simulation of millisecond control behavior may not legitimately rely solelyupon hourly averages.
Conversely, millisecond data may be unnecessary for a multi-decade
infrastructure simulation.
STDM therefore asks:
Is temporal resolution sufficient for the simulation question?
Sampling Frequency
Sampling Frequency defines how often observations are captured.For example:
100 Hz
means:
100 observations per second.
Sampling frequency may determine whether fast behavior is visible or lost.
Sampling Adequacy
Where relevant system dynamics occur faster than the available samplinginterval, material behavior may remain unobserved.
Therefore:
Available Frequency ≠ Sufficient Frequency
Temporal Granularity
Temporal granularity describes the level at which information is temporallygrouped.
For example:
individual transactions
may be aggregated into:
hourly totals
or:
daily averages.
Granularity changes may hide important temporal patterns.
Temporal Coverage
Temporal Coverage describes the period represented by the available input.Examples:
One Hour
Three Months
Five YearsComplete Operational Lifecycle
Coverage should be evaluated relative to the Simulation Purpose and Scope.Duration Sufficiency
A dataset may contain high-frequency observations while covering only a veryshort period.
High sampling density does not automatically establish sufficient duration
coverage.
SIMULOS® therefore distinguishes:
Observation Density
from:Observation Duration.
Seasonal Coverage
Where seasonal variation matters, the input basis should sufficientlyrepresent relevant seasonal conditions.
For example:
data collected only during summer may not sufficiently support simulation of
winter energy demand.
Cyclic Behavior
Relevant temporal cycles may include: daily, weekly, monthly, seasonal,annual, maintenance, production, economic, biological, or operational cycles.
STDM determines whether required cycles are sufficiently represented.
Temporal Variation
Simulation behavior may depend upon changes over time rather than staticvalues.
Relevant patterns may include: acceleration, degradation, growth, decay,
oscillation, accumulation, recovery, fatigue, adaptation, or escalation.
Trend Representation
Historical data may contain a trend.The simulation should determine whether that trend: remains relevant, has
stabilized, has reversed, has structurally changed, or should not be
extrapolated.
Regime Change
A Temporal Regime Change occurs where relationships represented by historicalinformation materially change.
Examples include: new regulation, new technology, infrastructure redesign,
market restructuring, environmental change, system upgrade, behavioral shift,
or operational-policy change.
Pre-Change ≠ Post-Change
SIMULOS® establishes:Data representative of one temporal regime must not automatically be treated
as representative of a materially different regime.
Temporal Drift
Temporal Drift occurs where the relationship between the input basis andcurrent or target reality gradually changes over time.
Drift may affect: distributions, behaviors, environmental conditions, system
performance, populations, or dependencies.
Temporal Gap
A Temporal Gap exists where required time periods contain insufficient ormissing information.
Examples include: missing sensor intervals, absent seasonal periods,
incomplete historical records, missing transition periods, or unobserved
operational phases.
Temporal Gap Materiality
Not every gap matters.A temporal gap becomes material where it could affect: simulated dynamics,
scenario behavior, output interpretation, validation, or downstream reliance.
Temporal Discontinuity
A discontinuity occurs where a time series changes abruptly because of:missing data, sensor replacement, system change, measurement-method change,
configuration change, or actual physical change.
The cause should remain distinguishable where material.
Stale Data
Stale Data is information whose temporal distance from the simulation context
may materially weaken its applicability.
Staleness is not determined solely by age.
It depends upon the rate at which the represented reality changes.
Stability-Relative Currency
A stable physical constant may remain temporally applicable indefinitely.A rapidly changing market variable may become stale within minutes.
Therefore:
Required Currency ∝ Rate of Material
Change
This relationship is conceptual, not numerical.Delayed Data
Data may describe a current event but arrive after a delay.Relevant delay may include: sensing delay, transmission delay, processing
delay, aggregation delay, publication delay, or ingestion delay.
Latency
Temporal Latency is the difference between a relevant event time and the time
at which the information becomes available to the simulation.
Latency may materially affect simulations involving: control, reaction,
coordination, autonomous decision-making, or real-time operation.
Latency Must Not Disappear
SIMULOS® establishes:Information available after an event must not be represented as information
that was available before the event where availability timing affects
simulated behavior.
Asynchronous Data
Inputs may arrive or be observed at different temporal frequencies.Asynchronous information may require: alignment, buffering, interpolation,
resampling, or explicit asynchronous modeling.
SDTM governs the transformation.
STDM governs whether the resulting temporal relationship is suitable.
Temporal Interpolation
Interpolation may fill values between known temporal observations.STDM governs the temporal consequence of using those values.
Interpolated time points should remain distinguishable from direct
observations where material.
Temporal Extrapolation
Temporal extrapolation estimates values beyond the observed period.Extrapolated values should not silently acquire the temporal status of
observations.
Historical Input
Historical information may be used to represent: prior conditions, baselinebehavior, long-term patterns, rare events, or model calibration.
Historical status should remain explicit.
Historical ≠ Current
SIMULOS® establishes:Historical relevance must be demonstrated where historical information is used
to represent current or future conditions.
Future-Oriented Simulation Inputs
Future simulations may depend upon: forecasts, projections, assumptions,synthetic trajectories, planned configurations, or predicted environmental
conditions.
Such information should remain distinguishable from historical observations.
Observed Time vs Projected Time
The simulation record should preserve whether a temporal input is:Observed
Estimated
Projected
Forecast
Assumed
or:
Synthetic
where the distinction is material.
Temporal Dependency
One input may depend upon the temporal state of another.For example:
Battery Temperature at Time t
may depend upon:Load during t−1 through t−n.
Static treatment may therefore fail to represent the actual dependency.
Lag Relationship
Some effects occur after a delay.Examples include: thermal response, economic response, biological response,
fatigue, infrastructure degradation, or control-system delay.
Where material, lag relationships should remain represented.
Lead Relationship
Some information may act as an advance indicator of another condition.Where such relationships are used in simulation, their temporal basis should
remain explicit.
Temporal Causality
STDM does not establish universal causal truth.It ensures that temporal relationships relied upon by the simulation do not
become detached from the sequence and timing assumptions upon which they
depend.
Temporal Distortion
A Temporal Distortion occurs where processing or input construction materiallychanges the timing characteristics of the represented phenomenon.
Examples include: smoothing away rapid events, shifting timestamps, averaging
across event boundaries, resampling beyond adequate resolution, or
synchronizing sources through unsupported assumptions.
Transformation-Induced Temporal Distortion
SDTM may identify the transformation.STDM evaluates its temporal consequence.
This separation preserves the boundary:
SDTM → What transformation occurred?
STDM → Is the resulting temporal representation still fit for simulation use?
Temporal Uncertainty
Temporal information may itself be uncertain.Examples include: approximate event time, clock error, synchronization
uncertainty, uncertain historical dating, variable latency, or estimated
temporal alignment.
STDM identifies temporal uncertainty as a distinct contribution to the broader
Simulation Input Uncertainty governed by SIUM.
Clock and Reference Uncertainty
Different systems may use: different clocks, different synchronizationmechanisms, different time zones, or imperfect timestamp precision.
Where material, these differences should be governed.
Temporal Boundary
A simulation may define explicit temporal boundaries.For example:
Simulation Period: 2026–2030
Input applicability outside or inside that period should be assessedaccordingly.
Temporal Scope Expansion
If the simulation period expands materially, previously accepted data mayrequire reassessment.
For example:
Original Scope: 2026
becoming:Expanded Scope: 2026–2040
may materially change the required temporal basis.Temporal Fitness
Temporal Fitness describes whether an input is sufficiently appropriate forthe temporal conditions of its intended simulation use.
Possible statuses may include:
Temporally Fit
Conditionally Temporally Fit
Partially Temporally Fit
Temporally Unfit
or:Temporal Fitness Undetermined Temporal
Fitness Is Context-Bound
A dataset may be temporally fit for one simulation and unfit for another.Therefore:
Temporal Fitness = Input Time Basis relative to Simulation Time Requirement
Temporal Condition
An input may remain usable under explicit temporal conditions.
For example:
Applicable only to system configuration before 1 January 2027.
or:
Suitable only where input latency remains below 50 ms.
Temporal Restriction
A temporal restriction narrows where or when the information may legitimatelybe used.
Restrictions should propagate downstream where they materially affect
simulation interpretation.
Temporal Limitation
A Temporal Limitation may include: insufficient duration, stale information,missing seasonal coverage, uncertain synchronization, unresolved regime
change, excessive latency, inadequate sampling frequency, temporal gaps, or
projected rather than observed values.
Temporal Reassessment Trigger
Reassessment may be required when: the Simulation Scope changes, the targettime period changes, system dynamics change, a new regime begins, new data
becomes available, sampling requirements change, latency changes,
synchronization changes, a new system version is introduced, or temporal
assumptions are materially revised.
No Temporal Inheritance Principle
SIMULOS® establishes:Temporal fitness must not be inherited automatically from a previous
simulation when the relevant time context has materially changed.
No Recency-to-Fitness Principle
Newer data is not automatically better data if it represents the wrongtemporal condition.
No Historical-to-Current Principle
Historical data does not automatically represent current reality.No Current-to-Future Principle
Current data does not automatically represent future reality.No Density-to-Duration Principle
A large number of observations collected over a short period does notestablish long-duration temporal coverage.
No Synchronization-by-Assumption Principle
Inputs from different clocks, systems, or periods must not be treated assynchronized merely because the simulation can technically combine them.
No Interpolation-to-Observation Principle
A temporally interpolated value remains generated information, not a directobservation.
No Latency Erasure Principle
A simulation must not silently give an actor, model, or system informationearlier than that information would legitimately have been available in the
represented reality.
Temporal Information Leakage
A particularly important failure occurs when future information isinadvertently introduced into an earlier simulated decision point.
Conceptually:
Information from t+1 → used at decision t
when the represented actor could not have known it.This creates Temporal Information Leakage.
No Future Knowledge Principle
SIMULOS® establishes:A simulated actor must not receive future information unless the Simulation
Purpose explicitly requires and identifies that condition.
This is especially important for: financial simulations, predictive systems,
autonomous decision systems, policy simulations, operational decision models,
and historical counterfactual analysis.
Temporal Leakage Control
Where decision-making is simulated, the information available at each decisionpoint should remain bounded by what would legitimately have been available at
that time.
Conceptually:
Decision at t ≤ Information Available at t
unless the scenario explicitly introduces another information condition.Temporal Snapshot Integrity
Where a simulation reconstructs a historical state, inputs should reflect acoherent temporal snapshot or explicitly governed multi-period reconstruction.
A historical simulation should not silently combine:
2022 knowledge + 2024 infrastructure + 2026 policy
and represent the result as:
Reality in 2022.
Temporal Input Set Coherence
Individual inputs may each be temporally acceptable while the complete InputSet is temporally incoherent.
STDM therefore assesses both:
Input-Level Temporal Fitness
and:Input-Set Temporal Coherence.
Temporal Coherence
A Simulation Input Set is temporally coherent where material inputs canlegitimately coexist within the represented simulation time context.
Temporal Conflict
A Temporal Conflict exists where two or more material inputs implyincompatible temporal states.
For example:
Infrastructure Version A active until June
while another input assumes:Infrastructure Version B active since March.
The conflict should be resolved, bounded, or preserved explicitly.
Temporal Fitness Propagation
Material temporal limitations should propagate downstream.Conceptually:
Temporal Limitation → Scenario / Execution Constraint → Output Interpretation
Limitation → Reliance Limitation
Later SIMULOS® Parent Standards govern those downstream stages.
STDM ensures the original temporal limitation remains visible.
Point-in-Time Reconstruction
STDM should enable questions such as:What data was available at this simulated moment?
Which system version did the data represent?
Were the sources synchronized?
Which values were observed and which were interpolated?
Did the simulation use information from the future?
Was the input still temporally applicable?
Temporal Traceability
STDM establishes:Simulation Input
↓Temporal Identity
↓Temporal Reference
↓Temporal Coverage
↓Temporal Alignment & Synchronization
↓Temporal Gaps / Drift / Regime Change
↓Temporal Fitness
↓Conditions / Restrictions
↓Simulation Use
Minimum Implementation Framework
1. Establish Temporal IdentityIdentify the material temporal properties of each Simulation Input. 2. Define
Simulation Temporal Requirements
Determine the: relevant period, resolution, frequency, duration, sequence, and
synchronization requirements.
3. Assess Temporal Applicability
Determine whether each input represents the required time context. 4. Assess
Temporal Alignment
Evaluate whether material inputs refer to sufficiently compatible temporal
states. 5. Assess Resolution and Sampling
Determine whether the available temporal resolution and sampling frequency can
represent materially relevant dynamics. 6. Assess Duration and Coverage
Determine whether the observation period covers required: cycles, seasons,
transitions, operating phases, and long-term behavior.
7. Identify Temporal Gaps and Distortions
Record: missing periods, discontinuities, stale data, latency, asynchronous
inputs, interpolation, extrapolation, and transformation-induced temporal
effects.
8. Assess Drift and Regime Change
Determine whether historical temporal relationships remain applicable to the
simulation context. 9. Control Temporal Information Leakage
Ensure simulated actors and systems receive only information legitimately
available at the represented decision time unless explicitly governed
otherwise. 10. Establish Temporal Conditions and Restrictions
Bound input use where temporal fitness is incomplete but manageable. 11.
Assign Temporal Fitness Status
Determine whether the input or Input Set is: Temporally Fit, Conditionally
Temporally Fit, Partially Temporally Fit, Temporally Unfit, or Temporal
Fitness Undetermined.
12. Preserve Temporal Traceability
Maintain:
Input → Time Basis → Alignment → Coverage → Limitation → Temporal Fitness →
Simulation Use
Governance Outputs
STDM may produce: Simulation Temporal Requirement, Temporal Input IdentityRecord, Temporal Reference Record, Observation-Time Record, Event-Time Record,
Effective-Time Record, Simulation Reference-Time Record, Temporal
Applicability Record, Temporal Currency Assessment, Temporal Alignment
Assessment, Cross-Source Temporal Alignment Record, Synchronization
Assessment, Synchronization Tolerance, Temporal Sequence Record, Temporal
Resolution Assessment, Sampling Frequency Assessment, Temporal Granularity
Record, Temporal Coverage Assessment, Duration Sufficiency Assessment,
Seasonal Coverage Record, Cyclic Behavior Coverage Record, Trend Applicability
Record, Regime Change Record, Temporal Drift Record, Temporal Gap Record,
Temporal Discontinuity Record, Stale Data Record, Latency Record, Asynchronous
Data Record, Temporal Interpolation Record, Temporal Extrapolation Record,
Temporal Dependency Record, Lag Relationship Record, Temporal Distortion
Record, Temporal Uncertainty Record, Temporal Information Leakage Record,
Temporal Snapshot Record, Temporal Conflict Record, Temporal Input Set
Coherence Assessment, Temporal Condition Record, Temporal Restriction Record,
Temporal Limitation Record, Temporal Fitness Status, Temporal Reassessment
Trigger, and Simulation Temporal Data Traceability Map.
Use Case 1 — Autonomous Driving Simulation
ScenarioAn autonomous-driving simulation combines: camera data captured at 30 Hz,
radar data captured at 20 Hz, vehicle telemetry captured at 100 Hz, and
pedestrian-location information updated every second.
The system being simulated makes control decisions every 50 milliseconds.
Application
STDM evaluates: sampling adequacy, synchronization, latency, event-time
alignment, interpolation, and what information would actually have been
available at each decision point.
The simulation is prohibited from using a pedestrian position update before
that update would have been available to the vehicle. Result
The Input Set receives a governed Temporal Fitness Status with explicit
synchronization tolerances and latency conditions.
The simulation can reconstruct the information state available to the
autonomous system at every material decision point. Use Case 2 — Energy Demand
Simulation
Scenario
A simulation estimates future electricity demand using three years of
historical consumption data.
The available dataset contains: strong summer coverage, incomplete winter
observations, a major industrial-demand shift in the final year, and no data
after a significant energy-pricing reform.
Application
STDM identifies: seasonal coverage weakness, a temporal regime change,
incomplete historical applicability, and limitations on projecting historical
relationships forward.
Result
The data may remain:
Conditionally Temporally Fit
for defined baseline analysis but insufficient for unrestricted future-demandrepresentation without additional temporal evidence or explicitly governed
assumptions.
Architectural Position
Simulation Temporal Data is the fourth internal governance space of theSimulation Input & Data Standard (SIDS).
The complete SIDS progression is:
Simulation Input Identification
↓Simulation Data Representativeness
↓Simulation Data Transformation
↓Simulation Temporal Data
↓Simulation Input Uncertainty
SIIM establishes:What information is entering the simulation?
SDRM establishes:
What reality can it legitimately represent?
SDTM establishes:
What happened to it before simulation use?
STDM establishes:
Is it temporally appropriate for the reality and dynamics being simulated?
The next module, SIUM — Simulation Input Uncertainty Module, governs the
uncertainty carried by the complete Simulation Input basis after
identification, representativeness, transformation, and temporal conditions
have been established.
Foundational Principle
Simulation data becomes temporally legitimate only when its time basis,sequence, resolution, coverage, synchronization, latency, applicability, and
limitations remain aligned with the temporal reality the simulation is
intended to represent.