README.md

---
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.