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

# traffic_weekly (TsFile format)

862 weekly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.

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/4656135
- Monash subset: `traffic_weekly`
- Modalities: Time-series
- Source series: 862
- Rows: 89,648 flattened timestamped observations
- Frequency: `weekly`
- Forecast horizon metadata: 8
- Missing-values metadata: False
- Equal-length metadata: True
- Missing target values preserved as NaN: 0
- Series length range: 104 to 104
- TsFile output: 1 file (traffic_weekly.tsfile)

## Files

- `traffic_weekly.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 `traffic_weekly`.

## Reading Example

```python
from tsfile import TsFileReader

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