wangdx25/oil030_sample

OIL-030 — Synthetic Global Oil Market Dynamics Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/oil030-sample.

Overview

A free, schema-identical preview of XpertSystems.ai's enterprise global oil market dynamics dataset for supply/demand balance forecasting, OPEC policy scenario analysis, geopolitical risk modeling, country-level production analytics, regional demand forecasting, and macro-coupled market modeling. The sample covers 10,958 days (~30 years) of global oil market data linking 15 producer countries (USA, Saudi Arabia, Russia, Canada, Iraq, China, Brazil, UAE, Kuwait, Iran, Nigeria, Norway, Mexico, Libya, Venezuela) and 6 consumer regions (North America, Europe, Asia Pacific, Middle East, Africa, Latin America), with 274,636 rows across 8 tables.

• Tables: 8 TsFile table(s), 274,636 rows in total.
  • futures_curve_structures: 10,958 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00 (UTC)
  • geopolitical_events: 326 rows, 1995-01-29 00:00:00 → 2024-12-27 00:00:00 (UTC)
  • global_demand: 65,748 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00.005000 (UTC)
  • inventory_balances: 10,958 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00 (UTC)
  • macroeconomic_factors: 10,958 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00 (UTC)
  • market_labels: 10,958 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00 (UTC)
  • opec_policy_scenarios: 360 rows, 1995-01-01 00:00:00 → 2024-12-01 00:00:00 (UTC)
  • production_supply: 164,370 rows, 1995-01-01 00:00:00 → 2024-12-31 00:00:00.014000 (UTC)

Schema (TsFile structure)

futures_curve_structures.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (4): front_month_price (double), month_6_price (double), month_12_price (double), curve_structure (string)

geopolitical_events.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (3): event_type (string), supply_impact_bpd (double), severity_score (double)

global_demand.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (5): region (string), transportation_demand_bpd (double), industrial_demand_bpd (double), petrochemical_demand_bpd (double), total_demand_bpd (double)

inventory_balances.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (3): inventory_million_barrels (double), daily_build_draw_million_barrels (double), storage_utilization_pct (double)

macroeconomic_factors.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (4): gdp_growth_pct (double), inflation_pct (double), usd_index (double), interest_rate_pct (double)

market_labels.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (3): market_direction (string), imbalance_score (double), volatility_score (double)

opec_policy_scenarios.tsfile

• Time (INT64, milliseconds) — the source meeting_date column.
• TAG: none — the table is stored as a single device.
• FIELD (3): scenario (string), quota_change_bpd (double), compliance_pct (double)

production_supply.tsfile

• Time (INT64, milliseconds) — the source date column.
• TAG: none — the table is stored as a single device.
• FIELD (5): country (string), production_bpd (double), spare_capacity_bpd (double), opec_factor (double), geopolitical_factor (double)

Conversion notes

• One TsFile per source CSV (8 tables); each table is stored as a single device, so the rig / well / tank / equipment / sensor identifiers stay ordinary FIELDs.
• Time = the table's own clock column as INT64 milliseconds (event timestamps, sample or inspection dates, and daily record dates). The clock columns used here are date, meeting_date.
• No column is dropped. Rows that share a timestamp are ordered by a 1 ms step in stable source order, so daily tables keep their exact calendar day.

Usage

Read this dataset with the Java SDK. It is written with the zstd compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (pip install tsfile) implements only UNCOMPRESSED, SNAPPY, GZIP and LZ4; opening this file with it yields no rows.

Read a converted file with the Apache TsFile Java SDK (org.apache.tsfile:tsfile):

import org.apache.tsfile.file.metadata.IDeviceID;
import org.apache.tsfile.read.TsFileReader;
import org.apache.tsfile.read.TsFileSequenceReader;
import org.apache.tsfile.read.common.Path;
import org.apache.tsfile.read.expression.QueryExpression;
import org.apache.tsfile.read.query.dataset.QueryDataSet;
import org.apache.tsfile.write.schema.MeasurementSchema;

import java.util.ArrayList;
import java.util.List;

String path = "futures_curve_structures.tsfile";
try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path);
     TsFileReader reader = new TsFileReader(sequenceReader)) {
    // Devices are enumerated as IDeviceID objects. Tables with TAG columns
    // have multi-segment device ids that cannot be rebuilt from their string
    // form, so always use getAllDevices() rather than getAllDeviceIds().
    for (IDeviceID device : sequenceReader.getAllDevices()) {
        List<Path> paths = new ArrayList<>();
        for (MeasurementSchema schema : reader.getMeasurement(device)) {
            String name = schema.getMeasurementName();
            if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) {
                paths.add(new Path(device, name, true));
            }
        }
        QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null));
        while (dataSet.hasNext()) {
            System.out.println(dataSet.next());
        }
    }
}

Source & license

• Original dataset: xpertsystems/oil030-sample
• Source card title: OIL-030 — Synthetic Global Oil Market Dynamics Dataset (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).