Datasets:
Tasks:
Time Series Forecasting
Modalities:
Time-series
Size:
n<1K
Tags:
language_creators:found
forecasting
benchmark
monash-time-series-forecasting-repository
monash-tsf
tsfile
License:
File size: 2,357 Bytes
d57bde9 c9ef20c d57bde9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | ---
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: solar_weekly (TsFile format)
configs:
- config_name: default
data_files:
- split: train
path: "*.tsfile"
modality:
- timeseries
size_categories:
- n<1K
---
# solar_weekly (TsFile format)
137 time series representing the weekly solar power production in Alabama state in 2006.
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/4656151
- Monash subset: `solar_weekly`
- Modalities: Time-series
- Source series: 137
- Rows: 7,124 flattened timestamped observations
- Frequency: `weekly`
- Forecast horizon metadata: 5
- Missing-values metadata: False
- Equal-length metadata: True
- Missing target values preserved as NaN: 0
- Series length range: 52 to 52
- TsFile output: 1 file (solar_weekly.tsfile)
## Files
- `solar_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 `solar_weekly`.
## Reading Example
```python
from tsfile import TsFileReader
reader = TsFileReader("solar_weekly.tsfile")
schemas = reader.get_all_table_schemas()
```
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