Multi-Scale Overlapping Pattern Tokenization (MSOPT)

Research Focus: Non-linear, non-stationary financial time series pattern tokenization and spatial representation learning.

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Warning

🔴 CORE PROBLEM STATEMENT

EXISTING TIME SERIES DEEP LEARNING ARCHITECTURES SUFFER FROM A FUNDAMENTAL PARADIGM FAILURE WHEN APPLIED TO NON-STATIONARY FINANCIAL MARKETS:

1. THE 1D SERIAL POINTWISE BLINDSPOT: Serial models (LSTMs, vanilla Transformers) evaluate scalar price points x_t sequentially. This destroys local visual shape context (inflections, wedges, double bottoms) and causes quadratic computational explosion O(T^2), preventing long historical context processing.
2. THE RIGID UNIFORM PATCHING BOTTLENECK: Modern patch transformers (PatchTST) force rigid uniform sequence slicing (P=16). In non-stationary markets, fixed boundaries clip pattern inflections arbitrarily, enforce single-scale rigidity, and lack translation invariance—rendering them fragile during market regime shifts.
3. THE QUANTITATIVE VISUAL GAP: Human quantitative traders analyze market dynamics visually as multi-scale 2D spatial chart primitives (micro-spikes, daily consolidations, macro regimes). Existing machine learning paradigms force 1D serial vectors or rigid 1D uniform grids, failing to extract localized multi-scale visual tokens.

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🔍 1. WHAT IS THERE (Current SOTA & Literature Base)

Existing state-of-the-art models in time series machine learning include:

• PatchTST (Nie et al., ICLR 2023): Slices univariate time series into uniform non-overlapping patches (P=16, S=8) and applies Vision Transformer self-attention.
• TimesNet (Wu et al., ICLR 2023): Discovers top-k dominant frequencies via FFT, reshapes 1D series into 2D period tensors, and applies 2D Inception convolutions.
• BORF (Spinnato et al., IEEE 2024): Extracts dilated receptive fields (w, d, s), quantizes subseries via 1D-SAX (mean + trend slope), and constructs Bag-of-Words histograms.
• VALMOD (Linardi et al., SIGMOD 2018): Computes variable-length Matrix Profiles across window lengths m in [m_min, m_max] using distance lower-bounding.

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🎯 2. WHY IT IS THERE (Design Intent & Strengths of Prior Art)

• PatchTST exists to solve the quadratic attention bottleneck of vanilla Transformers by reducing token count from T to T/P, speeding up compute by P^2.
• TimesNet exists to exploit powerful 2D Convolutional Neural Network (CNN) feature extractors by converting 1D sequences into 2D intra-period and inter-period matrices.
• BORF exists to provide an interpretable, highly effective symbolic dictionary classifier that captures non-contiguous dilated sub-patterns.
• VALMOD exists to discover exact repeating shape motifs across arbitrary scales without manual window tuning.

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🚫 3. WHAT IS NOT THERE (The Research Gap & Missing Capabilities)

Despite these advances, critical capabilities are completely missing for financial time series:

1. No Shift-Invariant Overlapping Tokenization: PatchTST's rigid stride S=8 clips patterns arbitrarily based on start index. If a chart pattern shifts by 2 days, token embeddings change completely.
2. No Multi-Scale Spatial Grid: TimesNet relies on FFT Fourier periods, assuming market dynamics are stationary and periodic. Financial markets are non-periodic and regime-shifting—FFT period detection fails completely.
3. No Sequence Order Retention in BORF: BORF compresses pattern frequencies into an unweighted Bag-of-Words histogram, discarding the crucial temporal sequence order of tokens (A -> B -> C).
4. No Unified Framework: No existing solution combines multi-scale dilated receptive field sampling, 1D-SAX codebook quantization, 2D Scale-Time Spatial Grid tensors, and deep Transformer attention.

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💡 4. WHAT WE ARE PROPOSING (The MSOPT Breakthrough Solution)

We propose Multi-Scale Overlapping Pattern Tokenization (MSOPT)—a 4-stage pipeline built specifically for non-stationary financial markets:

[1. RAW FINANCIAL MULTIVARIATE SERIES]
    (Log Returns, Parkinson Volatility, Relative Volume)
                 │
                 ▼
[2. DENSE MULTI-SCALE DILATED RECEPTIVE FIELDS]
    (Window w in {4,8,16,32}, Dilation d in {1,2,4}, Stride s=1)
                 │
                 ▼
[3. THRESHOLDED 1D-SAX SYMBOLIC DISCRETIZATION]
    (Segment Mean Quantization a_mu + Segment Slope Quantization a_beta)
                 │
                 ▼
[4. 2D SCALE-TIME SPATIAL TENSOR MAPPING]
    [Y-axis = Receptive Field Scale (w,d), X-axis = Time Index t]
                 │
                 ▼
[5. 2D SPATIAL CONVOLUTION & TRANSFORMER ENCODER]
    (Position + Scale + Volatility Multi-Dimensional Embeddings)
                 │
                 ▼
[6. DIRECTIONAL THRESHOLD & REGIME CLASSIFICATION]

Core Key Innovations:

• Dense Stride (s=1): Guarantees complete translation invariance across all pattern scales.
• 1D-SAX Codebook: Quantizes both segment mean level AND trend slope direction, eliminating noise while preserving shape.
• 2D Scale-Time Spatial Tensor: Maps multi-scale tokens into a 2D matrix (Y = Scales, X = Time), enabling 2D spatial convolutions to capture inter-scale pattern composition (e.g., micro-spikes triggering macro regime breaks).

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🛠️ 5. HOW WE ARE GOING TO DELIVER (Execution & Delivery Strategy)

Our delivery roadmap follows a rigorous 4-phase execution framework:

• PHASE 1: Literature Synthesis & Departmental Approval (COMPLETED)

  • 8 paper knowledge base in research_papers/ + INDEX.md
  • Formal Department Submission Proposal in RESEARCH_PROPOSAL.md
  • Workspace agent memory & continuous learning system in .agents/

• PHASE 2: Empirical Diagnostics & Baseline Infrastructure (COMPLETED)

  • Downloaded 15+ years daily OHLCV for SPY, AAPL, QQQ
  • Matrix Profile analysis (explore_matrix_profile.py) confirmed motif density concentrates at short scales (w in [5, 20], d_norm ~ 0.42-0.45)

• PHASE 3: Framework Construction & Controlled Ablation (WEEKS 3-5)

  • Implement MSOPT Tokenizer & 2D Scale-Time Spatial Embedder (src/tokenizer/)
  • Build 2D Conv-Transformer Neural Backbone (src/models/)
  • Controlled walk-forward ablation vs PatchTST, TimesNet, BORF, and LightGBM

• PHASE 4: Cross-Asset Backtesting & Academic Publication (WEEKS 6-8)

  • Walk-forward expanding window backtesting with explicit 5 bps transaction cost
  • Out-of-Sample evaluation across SPY, AAPL, QQQ, TLT
  • Drafting academic manuscript for top-tier submission

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📂 Repository Navigation

• 📄 Official Department Proposal: RESEARCH_PROPOSAL.md
• 📚 Literature Research Library: research_papers/INDEX.md
• 📐 Master Architecture Spec: research_papers/08_Proposed_MultiScale_Dynamic_Token_Architecture.md
• 🤖 Agent Memory & Guidelines: .agents/AGENTS.md
• 📊 Empirical Experiment Log: .agents/EXPERIMENT_LOG.md