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Dataset Overview

Seahorse reads event sequence data from JSONL split files — one JSON object per line, one line per sequence. The same format works for single-model runs, benchmark campaigns, and Hugging Face sources.

What a Dataset Needs

A complete dataset for a Seahorse run consists of three split files:

File Required for Notes
train.jsonl fit, tune, bench Training sequences
val.jsonl fit, tune, bench Validation sequences — drives early stopping
test.jsonl evaluation Held-out test sequences

Each file is one JSON object per line. Each object is one event sequence.

Minimal Record

{"times": [0.1, 0.4, 1.2], "locations": [[0.2, 0.4], [0.3, 0.8], [0.7, 0.1]]}

times[i] and locations[i] describe the same event. locations must be a list of 2D coordinate pairs.

Data Sources

Source How to use
Curated seahorse-stpp datasets load_dataset("citibike") or --dataset citibike (short name)
Any Hugging Face repo Pass --dataset owner/repo[/subdir] — Seahorse downloads and caches
Local JSONL files Pass --train, --val, --test flags or a directory with --dataset
Local split collection Point --splits_dir at a root with one subdirectory per dataset

See Data Format for the full contract and command support matrix.

Ready-to-use Datasets

Seahorse curates 13 real-world STPP datasets in the seahorse-stpp Hugging Face organization — spanning urban mobility, crime, natural hazards, public health, social check-ins, and even neuroimaging — all in the same JSONL split format. Load any of them by its short name:

from seahorse.data import load_dataset

splits = load_dataset("citibike")  # downloads + caches

Browse the full collection, with load snippets and each dataset's space/time axes, in the Dataset Catalog.

Need controlled ground truth? Seahorse's synthetic benchmark sequences are generated with HawkesNest — its entanglement suite produces spatio-temporal data with tunable space–time coupling for stress-testing models. See Synthetic benchmark suites.

Next Steps

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