4. Advanced Time Series Reference Architecture - stanlypoc/AIRA GitHub Wiki

Advanced Time Series Reference Architecture

1. Introduction

1.1 Purpose

Framework for implementing time series solutions across capability levels (Batch → Real-time → Autonomous)

1.2 Audience

  • Data Engineers
  • ML Engineers
  • IoT Architects
  • Quantitative Analysts

1.3 Scope & Applicability

In Scope:

  • High-frequency forecasting
  • Anomaly detection
  • Multivariate sequence modeling

Out of Scope:

  • Non-temporal data analysis
  • Hardware-specific sensor protocols

1.4 Assumptions & Constraints

Prerequisites:

  • Python 3.9+ with NumPy/Pandas
  • Understanding of ARIMA/LSTM architectures

Technical Constraints:

  • Minimum 1ms timestamp precision

  • 10,000 events/sec ingestion capability

Ethical Boundaries:

  • No predictive policing applications
  • Bias testing for financial forecasting models

1.6 Example Models

Level Forecasting Anomaly Detection Simulation
Level 2 Prophet, ARIMA Isolation Forest Monte Carlo
Level 3 N-BEATS, TFT USAD, GAN-based Agent-based Modeling
Level 4 DeepGLO, AutoTS Neuromorphic EDGE Digital Twin Systems

2. Architectural Principles

Here are the Architecture Principles for Time Series (Advanced) systems, designed for enterprise-scale forecasting, anomaly detection, and autonomous decision-making. These principles apply to both batch and real-time time series workloads, across cloud, edge, and hybrid platforms.

📈2.1 Foundational Architecture Principles for Time Series (Advanced)

1. Temporal Integrity

Preserve the order and continuity of time in every pipeline component.

  • Ensure strict timestamp alignment and time zone consistency.
  • Avoid data leakage by enforcing train/test temporal separation.
  • Use sliding windows or expanding windows with causal constraints.

2. Granularity Awareness

Architect for multi-resolution time series, from milliseconds to months.

  • Design pipelines to dynamically resample or interpolate data.
  • Handle variable frequency and missing intervals natively.
  • Support hierarchical time series (e.g., region → city → store).

3. Efficient Feature Engineering

Optimize for lag, rolling, seasonal, and calendar features.

  • Use dedicated time series libraries (e.g., tsfresh, Kats, GluonTS).
  • Cache computed features to avoid recomputation in streaming setups.
  • Integrate external covariates (e.g., holidays, weather, promotions).

4. Model Diversity

Support a mix of statistical and deep learning models.

  • Enable baseline models (ARIMA, ETS) and ML/DL models (DeepAR, TFT, N-BEATS).
  • Use ensemble or hybrid models when appropriate.
  • Incorporate uncertainty estimation (e.g., prediction intervals, quantile forecasts).

5. Forecastability Analysis

Assess and classify forecastability before modeling.

  • Apply metrics like Coefficient of Variation, autocorrelation strength, or seasonality scores.
  • Route different time series to different model classes (low vs. high volatility).

6. Real-Time Readiness

Build pipelines that support both batch and real-time inference.

  • Use a unified architecture for real-time (streaming) and historical data.
  • Include signal smoothing, real-time anomaly detection, and immediate alerts.
  • Enable online learning for model updates without full retraining.

7. Explainability & Trust

Provide transparent and interpretable forecasts.

  • Log feature importances, confidence intervals, and input anomalies.
  • Visualize trend, seasonality, and residual components.
  • Expose interpretable surrogate models for non-technical stakeholders.

8. Drift & Degradation Monitoring

Continuously monitor for data, concept, and prediction drift.

  • Track distribution changes, model accuracy decay, and input volatility.
  • Automate alerts and retraining triggers based on statistical thresholds.
  • Include rollback policies to revert to last stable model.

9. Temporal Fusion & Alignment

When fusing with other modalities (e.g., images, events), ensure temporal context is preserved.

  • Synchronize timestamps across modalities.
  • Store fused embeddings in time-indexed structures (e.g., time-aware vector stores).

10. Scalability & Modularity

Design for multi-tenant, multi-series scaling.

  • Use time series databases (e.g., InfluxDB, TimescaleDB) or Delta Lake.
  • Batch inference using parallelizable pipelines.
  • Modularize ingestion, feature extraction, model serving, and evaluation.

11. Feedback-Driven Evolution

Support human-in-the-loop corrections and feedback loops.

  • Allow manual forecast overrides with traceable audit logs.
  • Use feedback to fine-tune or re-weight model outputs.

12. Ethical and Context-Aware Forecasting

Understand the real-world impact of automated forecasts.

  • Be transparent about model uncertainty in decision-making.
  • Avoid automation in life-critical or irreversible domains without human review.
  • Comply with data governance, fairness, and transparency standards.

2.2 Standards Compliance

  1. Security & Privacy

    • Must comply with: IEC 62443-3-3 (IIoT), FIPS 140-3
    • Practical tip: Hardware-encrypted time windows

  2. Ethical AI

    • Key standards: IEEE 7006 (Temporal Data Ethics)
    • Checklist item: Backtest fairness across demographic segments

2.3 Operational Mandates

5 Golden Rules:

  1. Never interpolate missing security telemetry
  2. Maintain exact event time sequencing
  3. Three-version model consensus for critical predictions
  4. Cold/warm/hot data tiering by access patterns
  5. Immutable audit trails with NTP-synchronized timestamps

Sample Audit Log:

{
  "event_time": "2023-11-20T14:23:12.345Z",
  "model": "tft-1.0",
  "input_window": "2023-11-20T14:22:00Z to 14:23:00Z",
  "prediction": {"value": 42.7, "confidence": 0.92},
  "drift_score": 0.03
}

3. Architecture by Technology Level

3.1 Level 2 (Basic) - Batch Forecasting

Definition:
Scheduled periodic forecasting jobs

Key Traits:

  • Fixed frequency retraining
  • Single series prediction
  • Point estimates only

Logical Architecture:

graph LR
    A[Historical Data] --> B[Feature Engineering]
    B --> C[Model Training]
    C --> D[Batch Prediction]
    D --> E[Results Storage]

Cloud Implementations:

Provider Services Key Features
Azure Data Explorer + ML Studio Built-in ARIMA
AWS Forecast + Timestream AutoML for TS
GCP BigQuery ML + Dataflow SQL-based TS functions

3.2 Level 3 (Advanced) - Real-Time Analytics

Definition:
Streaming analysis with sub-second latency

Key Traits:

  • Adaptive windowing
  • Concept drift detection
  • Multivariate analysis

Logical Architecture:

graph LR
    A[Data Stream] --> B[Windowing Engine]
    B --> C[Online Feature Store]
    C --> D[Ensemble Model]
    D --> E[Alert Generator]
    E --> F[Action Queue]

Critical Components:

  • Streaming PCA for dimensionality reduction
  • Change point detection (Bayesian Online)
  • Model explainability (SHAP over windows)

3.3 Level 4 (Autonomous) - Self-Learning Systems

Definition:
Continuously adapting temporal systems

Key Traits:

  • Automatic feature discovery
  • Dynamic architecture adjustment
  • Causal inference

Logical Architecture:

graph LR
    A[Raw Streams] --> B[Meta-Learner]
    B --> C[Feature Optimizer]
    C --> D[Model Architect]
    D --> E[Causal Validator]
    E --> F[Production Ensemble]

Safety Mechanisms:

  • Prediction stability monitor
  • Counterfactual analysis layer
  • Automated rollback on N-sigma events

4. Glossary & References

Terminology:

  • Concept Drift: Changing data patterns over time
  • Granger Causality: Statistical test for time-based causation

References:

  1. Time Series ML Benchmark
  2. IEC 61508 (Functional Safety of Temporal Systems)