Datasets:

THULab
/

macroecon

license: cc-by-nc-nd-4.0
task_categories:
  - time-series-forecasting
language:
  - en
tags:
  - tsfile
  - timeseries
  - time-series
  - macroeconomics
  - forecasting
  - economics
  - finance
  - format:tsfile
pretty_name: MacroEconHorizon (TsFile)
size_categories:
  - 1M<n<10M

MacroEconHorizon (TsFile)

Apache TsFile version of bkoyuncu/MacroEcon.

Overview

MacroEconHorizon is a curated collection of macroeconomic time series spanning 63 countries (plus the euro area and South Africa, 64 country files in total) from 1970 to 2023. It is designed to support nowcasting, forecasting, and scenario analysis for machine-learning researchers and economic policy makers. Indicators cover GDP, inflation, unemployment rates, commodity prices, financial market variables, and monetary policy metrics, curated by the Bank for International Settlements (BIS).

Each variable is identified by a code, with a frequency (D=Daily, M=Monthly, Q=Quarterly) and a transformation (value, _delta, _log, _delta_log). Frequencies are standardized to a daily grid via forward-fill.

Countries: 64 (63 countries + euro area)
Time span: 1970 to 2023 (daily grid)
Rows: ~1.3 million across all country panels
Indicators: GDP, inflation, unemployment, commodity prices, financial variables, monetary policy metrics

Schema (TsFile structure)

Because the feature columns are country-specific (the indicator set does not overlap across countries), each country is converted to its own TsFile / table macroecon_<cc> rather than a single sparse union table.

Time (INT64, milliseconds) — the daily date (source Unnamed: 0 column).
Q_... / D_... / M_... / A_... (FLOAT) — the economic indicators. Column names encode frequency, code, country, series row, and transformation (e.g. Q_FXSP_AE_row_0_value_delta_log); dots/special characters are replaced with underscores.

Each country is a single device; its indicators are numeric FIELD columns. Missing values (forward-fill does not reach the start of every series) are stored as null.

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("macroecon_ae.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/bkoyuncu/MacroEcon
Author / publisher: bkoyuncu
Curated by: Bank for International Settlements (BIS)
License: CC BY-NC-ND 4.0