Enhance Adaptive Prediction for Long-Range Context and Dynamic Weighting

[axelvyrn]

Summary

While TiresiasIQ v3 already blends ML, temporal priors, and semantic similarity for adaptive predictions, there are opportunities to further improve accuracy and context-awareness. The current system works well for short-term contexts and action normalization, but struggles with:

• Long-range dependencies (older logs fade too quickly).
• Balancing weights (w_model, w_temporal, w_semantic) dynamically.
• More nuanced temporal behaviors (seasonality, holidays, user-specific cycles).
• Richer context capture beyond keyword and embeddings.

Proposed Advancements

1. Dynamic Weight Adjustment

• Implement adaptive weighting between model / temporal / semantic based on recent validation accuracy.
• Use Bayesian optimization or reinforcement-style updates to tune w_model, w_temporal, w_semantic automatically.

2. Long-Term Memory Integration

• Introduce a hierarchical memory system:
  • Short-term memory (recent logs, already present).
  • Mid-term trends (sliding windows, weeks/months).
  • Long-term priors (user habits, seasonality).
• Use decay functions (exponential or power-law) instead of abrupt cutoffs.

3. Enhanced Temporal Modeling

• Incorporate seasonality features (monthly, quarterly, yearly cycles).
• Detect user-specific periodicities (e.g., daily vs. weekly patterns).
• Integrate holiday/weekend calendars for stronger time priors.

4. Semantic Context Expansion

• Move beyond single-action embeddings to include context windows (e.g., last 3–5 actions).
• Explore transformer-based sequence encoders for log history.
• Improve action normalization with better lemmatization + synonym mapping (WordNet or transformer embeddings).

5. Drift-Aware Retraining Hooks

• Instead of only Page-Hinkley flags, enable auto-scheduled retraining or few-shot reweighting when drift is detected.
• Add GitHub Actions / cron hooks to periodically evaluate drift across users.

6. Evaluation Framework

• Create a benchmarking suite for accuracy:
  • Compare pure ML, temporal-only, semantic-only, and hybrid predictions.
  • Measure improvements from each advancement on held-out logs.

Expected Outcome

• More accurate, context- and time-aware predictions.
• Reduced reliance on fixed weights → more self-optimizing behavior.
• Better generalization to both new users and long-term patterns.

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To-Do

• Implement dynamic weight tuning
• Add mid/long-term memory layers
• Expand temporal priors with seasonality + holidays
• Add multi-action context embeddings
• Hook retraining triggers to drift detection
• Build evaluation/benchmark pipeline

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Impact:
This would elevate TiresiasIQ from being a "smart predictor" into a fully adaptive, self-correcting intelligence layer that learns when, why, and how actions happen — not just what comes next.