Phase 64: Temporal Sequence Modeling & Time Series Intelligence — Planning & Architecture

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Phase 64 introduces Temporal Sequence Modeling & Time Series Intelligence — a comprehensive framework for extracting patterns from sequential data, forecasting future values, detecting anomalies, and classifying temporal patterns.

Research Foundations

• Temporal Fusion Transformer (Lim et al. 2021) — Multi-horizon forecasting with interpretable attention, variable selection networks, and static/temporal covariate handling
• Informer (Zhou et al. 2021) — ProbSparse self-attention for long sequence time-series forecasting with O(L log L) complexity
• Autoformer (Wu et al. 2021) — Auto-correlation mechanism replacing self-attention with series-level decomposition
• PatchTST (Nie et al. 2023) — Channel-independent patching strategy treating time series subsequences as tokens
• TimeGPT (Garza & Mergenthaler-Canseco 2023) — Foundation model for time series, pre-trained on massive temporal corpora
• N-BEATS (Oreshkin et al. 2020) — Neural Basis Expansion Analysis with interpretable trend/seasonality stacks
• DeepAR (Salinas et al. 2020) — Autoregressive RNN for probabilistic forecasting
• TCN (Bai et al. 2018) — Dilated causal convolutions with residual connections
• S4/Mamba — Structured state spaces for long-range temporal dependencies with linear complexity
• Probabilistic Forecasting — Conformal prediction, quantile regression, normalizing flows

Sub-phases

Sub-phase	Component	Focus
64.1	TemporalFeatureExtractor	Multi-scale temporal feature extraction with wavelet decomposition, STL, learnable embeddings
64.2	SequenceForecaster	Autoregressive and non-autoregressive forecasting with Transformer variants
64.3	AnomalyDetector	Temporal anomaly detection via reconstruction, attention scores, statistical process control
64.4	TimeSeriesClassifier	Pattern classification with InceptionTime, ROCKET, temporal attention
64.5	TemporalIntelligenceOrchestrator	End-to-end pipeline with online learning and adaptation

Architecture

Raw Time Series → TemporalFeatureExtractor → Feature Representations
                                                    ↓
                              ┌──────────────────────┼──────────────────────┐
                              ↓                      ↓                      ↓
                     SequenceForecaster      AnomalyDetector      TimeSeriesClassifier
                              ↓                      ↓                      ↓
                     Forecasts + CI          Anomaly Scores         Pattern Labels
                              └──────────────────────┼──────────────────────┘
                                                     ↓
                                    TemporalIntelligenceOrchestrator

Key Design Principles

1. Multi-scale temporal modeling — Patterns at multiple horizons (seconds to years)
2. Probabilistic by default — Uncertainty quantification on all outputs
3. Online learning — Continuous adaptation as new data arrives
4. Foundation model ready — Supports pre-trained temporal foundation models
5. Interpretable decomposition — Separate trend, seasonality, residual

Integration Points

• Phase 61 (Data-Centric AI) — Data quality for temporal pipelines
• Phase 56 (Explainable AI) — Temporal feature attribution
• Phase 60 (Efficient Inference) — Real-time temporal serving
• Phase 52 (RAG) — Historical pattern retrieval

References

1. Lim et al. (2021). "Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting"
2. Zhou et al. (2021). "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting" — AAAI 2021
3. Wu et al. (2021). "Autoformer: Decomposition Transformers with Auto-Correlation" — NeurIPS 2021
4. Nie et al. (2023). "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers" — ICLR 2023
5. Garza & Mergenthaler-Canseco (2023). "TimeGPT-1" — arXiv:2310.03589
6. Oreshkin et al. (2020). "N-BEATS: Neural Basis Expansion Analysis for Time Series Forecasting" — ICLR 2020
7. Salinas et al. (2020). "DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks"
8. Bai et al. (2018). "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling"
9. Gu et al. (2022). "Efficiently Modeling Long Sequences with Structured State Spaces" — ICLR 2022
10. Gu & Dao (2023). "Mamba: Linear-Time Sequence Modeling with Selective State Spaces"