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name: sequential description: "Route SDV PAR sequential synthesis workflows for sequence-keyed data." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

SDV sequential synthesis router

Use this sub-skill when the task is about SDV time series or sequential data where multiple rows belong to a sequence identified by a sequence key. The main owned model is PARSynthesizer.

Route here for

• Natural requests such as "PARSynthesizer", "sequence key", "sequence index", "context columns", "time series SDV", sample_sequential_columns, or get_random_sequence_subset.
• Building one-table sequential metadata that declares a sequence key and, optionally, a datetime or numerical sequence index.
• Fitting PAR, sampling whole new sequences, or generating sequential columns for known context rows.
• Reducing a long sequential table before fitting while preserving sequence membership.
• PAR-specific save/load, transformer updates, constraint compatibility, and cuda behavior.

Route elsewhere

• Ordinary non-sequential single-table modeling: use the single-table sub-skill.
• Metadata primitives, local I/O, generic metadata validation, visualization, and broad data cleaning: use the data-preparation sub-skill.
• Constraint class design and programmable constraint internals: use the constraints sub-skill, then return here to attach compatible constraints to PAR.
• Quality metrics, diagnostics, or plots comparing real and synthetic data: use the evaluation sub-skill.

Operating path

1. Confirm the data is a single sequential table, not just a flat table with a timestamp. A valid PAR workflow needs a sequence key column whose values group rows into independent sequences.
2. Build or inspect metadata with sequence_key; add sequence_index only when row order/timing matters and the column is datetime or numerical.
3. Identify context_columns: columns that remain constant within each sequence. Never include the sequence key itself as a context column.
4. Validate that each context column has exactly one value per sequence key before fitting; if not, fix the data or remove that context column.
5. Instantiate PARSynthesizer with a small epochs value for quick smoke work and a larger value for production modeling. Set cuda=False when deterministic CPU-only behavior matters or CUDA/torch availability is uncertain.
6. Use sample(num_sequences, sequence_length=None) for new sequence contexts. Use sample_sequential_columns(context_columns, sequence_length=None) only when the synthesizer was created with context columns and the caller supplies one context row per desired sequence.
7. For long data, call get_random_sequence_subset before fitting; verify the resulting sequence count and per-sequence lengths before handing the subset to PAR.
8. Attach constraints before fit and only when every constraint covers either context columns only or non-context sequential columns only; overlapping constraints on the same columns are not supported.
9. Save/load with PAR's inherited save and load methods after checking whether the target runtime has compatible torch/CUDA support.

References

• API reference: PAR constructor, methods, metadata helpers, and sequence-subset utility signatures.
• Workflows: sequence metadata setup, fit/sample, known-context generation, long-sequence subsetting, constraints, and save/load recipes.
• Troubleshooting: sequence-specific errors and fixes.