---
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: 18154839
    num_examples: 210
  - name: validation
    num_bytes: 18235479
    num_examples: 210
  - name: test
    num_bytes: 18316119
    num_examples: 210
  download_size: 47737715
  dataset_size: 54706437
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---
# Dataset Card for "kdd210_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/kdd210_hourly")
```

**Dataset Card for Air Quality in KDD cup 2018**

Originally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.

**Preprocessing information**:
- Grouped by hour (frequency: "1H").
- Applied Standardization as preprocessing technique ("Std").
- Preprocessing steps:
  1. Standardizing data.
  2. Replacing NaN values with zeros.
    
**Dataset information**:
- Missing values are converted to zeros.
- Number of time series: 210
- Number of training samples: 10802
- Number of validation samples: 10850 (number_of_training_samples + 48)
- Number of testing samples: 10898 (number_of_validation_samples + 48)

**Dataset format**:
```python
  Dataset({
  
      features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
      
      num_rows: 210
      
  })
```
**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(2017, 1, 1, 14, 0, 0),
 'feat_static_cat': [0],
 'feat_dynamic_real': None,
 'item_id': 'T1',
 'target': [ 1.46812152,  1.31685537,  1.26169969, ...,  0.47487208, 0.80586637,  0.33006964]
}
```

**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.