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---
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
- name: target
sequence: float64
splits:
- name: train
num_bytes: 120352440
num_examples: 862
- name: validation
num_bytes: 120683448
num_examples: 862
- name: test
num_bytes: 121014456
num_examples: 862
download_size: 124542918
dataset_size: 362050344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "traffic_hourly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
**Download the Dataset**:
```python
from datasets import load_dataset
dataset = load_dataset("LeoTungAnh/traffic_hourly")
```
**Dataset Card for Electricity Consumption**
this dataset encompasses 862 hourly time series data points revealing the road occupancy rates across freeways in the San Francisco Bay area from 2015 to 2016.
**Preprocessing information**:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
**Dataset information**:
- Number of time series: 862
- Number of training samples: 17448
- Number of validation samples: 17496 (number_of_training_samples + 48)
- Number of testing samples: 17544 (number_of_validation_samples + 48)
**Dataset format**:
```python
Dataset({
features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
num_rows: 862
})
```
**Data format for a sample**:
- 'start': datetime.datetime
- 'target': list of a time series data
- 'feat_static_cat': time series index
- 'feat_dynamic_real': None
- 'item_id': name of time series
**Data example**:
```python
{'start': datetime.datetime(2015, 1, 1, 0, 0, 1),
'feat_static_cat': [0],
'feat_dynamic_real': None,
'item_id': 'T1',
'target': [-0.7127609544951682, -0.6743409178438863, -0.3749847989359815, ... 0.12447567753068307,...]
}
```
**Usage**:
- The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
- Other algorithms can extract data directly by making use of 'target' feature. |