Datasets:

THULab
/

computational_phase_transitions_data

license: mit
task_categories:
  - time-series-forecasting
  - tabular-classification
tags:
  - tsfile
  - timeseries
  - time-series
  - finance
  - econophysics
  - algorithmic-information-theory
  - structural-breaks
  - anomaly-detection
  - format:tsfile
pretty_name: Computational Phase Transitions (TsFile)
size_categories:
  - 10M<n<100M

Financial Structural Breaks & Regime Detection Benchmark (TsFile)

Apache TsFile version of algoplexity/computational-phase-transitions-data.

Overview

Large-scale collection of non-stationary, continuous financial time series as an immutable data artifact for the Algoplexity research program into Algorithmic Information Dynamics (AID) in financial markets. Each id is a trajectory — an ordered sequence of (time, value) pairs under a time-varying control parameter period — with a per-trajectory boolean label structural_breakpoint (whether the trajectory exhibits a structural/phase break). The source is split into train and a reduced test set; that split is preserved. Because the train split is very large (23.7M rows), it is stored as six sharded files (*_train.tsfile, *_train_1.tsfile, …, *_train_5.tsfile) that together form one logical table.

Schema (TsFile structure)

Time (INT64, milliseconds) — the step index within each trajectory (not wall-clock time).
id (TAG) — the trajectory identifier.
structural_breakpoint (TAG) — the per-trajectory label (True/False).
period (FIELD, INT64) — the time-varying control parameter.
value (FIELD, FLOAT) — the trajectory value.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("computational_phase_transitions_data_test.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

Original dataset: https://huggingface.co/datasets/algoplexity/computational-phase-transitions-data
Author / publisher: algoplexity
License: MIT