Add TsFile (converted from Real-TSF/TIME-ProcessedCSV)

README.md

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+---
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+tags:
+- tsfile
+- timeseries
+- time-series
+- format:tsfile
+pretty_name: Commodity_Production
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: commodity_production.tsfile
+---
+
+# Commodity_Production (TsFile)
+
+Apache TsFile version of the `Commodity_Production` sub-dataset of [`Real-TSF/TIME-ProcessedCSV`](https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV).
+
+## Overview
+
+`Commodity_Production` is one of the processed time-series collections bundled in TIME-ProcessedCSV, a multi-domain repository of cleaned CSV series (energy, transport, weather, finance, health and more), each with a `timestamp` column and one or more measurement columns per file.
+
+- **Source files:** 32 CSV file(s) under `https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV/tree/main/Commodity_Production`
+- **Converted rows:** 10,403 (long format: one row per measurement)
+- **Data files:** `['commodity_production.tsfile']`
+
+## Schema (TsFile structure)
+
+- **Time** (INT64, milliseconds) — the source `timestamp` column.
+- **freq** (TAG) — source sampling-frequency directory (e.g. `H`, `15T`, `D`).
+- **series** (TAG) — source file stem (e.g. `item0`, `NRSROT`).
+- **channel** (TAG) — source measurement column name.
+- **measurement** (FIELD, FLOAT) — the measurement value.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("commodity_production.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/Real-TSF/TIME-ProcessedCSV/tree/main/Commodity_Production
+- Bundle: https://huggingface.co/datasets/Real-TSF/TIME-ProcessedCSV
+- License: apache-2.0