README.md

---
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
- multivariate-time-series-forecasting
tags:
- language_creators:found
- forecasting
- benchmark
- monash-time-series-forecasting-repository
- monash-tsf
- tsfile
- modality:timeseries
- timeseries
- format:tsfile
pretty_name: car_parts (TsFile format)
configs:
- config_name: default
  data_files:
  - split: train
    path: "*.tsfile"
modality:
- timeseries
size_categories:
- 1K<n<10K
---

# car_parts (TsFile format)

2674 intermittent monthly time series that represent car parts sales from January 1998 to March 2002.

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/4656022
- Monash subset: `car_parts`
- Modalities: Time-series
- Source series: 2,674
- Rows: 136,374 flattened timestamped observations
- Frequency: `monthly`
- Forecast horizon metadata: not specified
- Missing-values metadata: True
- Equal-length metadata: True
- Missing target values preserved as NaN: 6,122
- Series length range: 51 to 51
- TsFile output: 1 file (car_parts.tsfile)

## Files

- `car_parts.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 `car_parts`.

## Reading Example

```python
from tsfile import TsFileReader

reader = TsFileReader("car_parts.tsfile")
schemas = reader.get_all_table_schemas()
```