Datasets:
Tasks:
Time Series Forecasting
Modalities:
Time-series
Sub-tasks:
univariate-time-series-forecasting
License:
File size: 3,088 Bytes
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license: cc-by-4.0
annotations_creators:
- no-annotation
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
tags:
- tsfile
- format:tsfile
- timeseries
- time-series
pretty_name: wind_4_seconds (TsFile format)
configs:
- config_name: default
data_files:
- split: train
path: "*.tsfile"
---
# wind_4_seconds (TsFile format)
A single very long daily time series representing the wind power production in MW recorded per every 4 seconds starting from 01/08/2019.
This repository contains the full source `.tsf` series from the Monash Time Series Forecasting Repository converted to [Apache TsFile](https://tsfile.apache.org/) format.
## Summary
- Source dataset: [`Monash-University/monash_tsf`](https://huggingface.co/datasets/Monash-University/monash_tsf)
- Original source: https://zenodo.org/record/4656032
- Monash subset: `wind_4_seconds`
- Modalities: Time-series
- Source series: 1
- Rows: 7,397,147 flattened timestamped observations
- Frequency: `4_seconds`
- Forecast horizon metadata: not specified
- Missing-values metadata: False
- Equal-length metadata: True
- Missing target values preserved as NaN: 0
- Series length range: 7,397,147 to 7,397,147
- TsFile output: 8 files (wind_4_seconds_1.tsfile .. wind_4_seconds_8.tsfile)
## Files
- `wind_4_seconds_1.tsfile`
- `wind_4_seconds_2.tsfile`
- `wind_4_seconds_3.tsfile`
- `wind_4_seconds_4.tsfile`
- `wind_4_seconds_5.tsfile`
- `wind_4_seconds_6.tsfile`
- `wind_4_seconds_7.tsfile`
- `wind_4_seconds_8.tsfile`
## TsFile Schema
| Column | Role | TsFile type |
|---|---|---|
| `Time` | TIME | INT64 |
| `series_id` | TAG | STRING |
| `series_name` | TAG | STRING |
| `start_timestamp` | TAG | STRING |
| `target` | FIELD | FLOAT |
## Conversion Notes
- Each source `.tsf` data row is stored as one TsFile device.
- Source `.tsf` attributes are stored as TAG columns.
- The `target` series values are flattened into timestamped rows and stored as a FLOAT FIELD.
- `Time` is synthesized from the source start timestamp and the `.tsf` frequency metadata, with millisecond precision.
- Large outputs may be sharded by the TsFile conversion tool; all listed shards belong to the same logical table `wind_4_seconds`.
## 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("wind_4_seconds_1.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]
print("columns:", [c.get_column_name() for c in table.get_columns()])
with reader.query_table(table_name, ["target"], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
```
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