> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

> Download and wrangling utility for long-horizon datasets. These datasets have been used by `NHITS, AutoFormer, Informer, PatchTST, TiDE` among many other neural forecasting methods. The datasets include the original [ETTh1, ETTh2, ETTm1, ETTm2, Weather, ILI, TrafficL](https://github.com/zhouhaoyi/ETDataset) benchmark datasets.

# Long-Horizon Original Datasets

##

### `Weather`

```python theme={null}
Weather(freq='10M', name='weather', n_ts=21, test_size=10539, val_size=5270, horizons=(96, 192, 336, 720))
```

This Weather dataset contains the 2020 year of 21 meteorological
measurements
recorded every 10 minutes from the Weather Station of the Max Planck Biogeochemistry
Institute in Jena, Germany.

Reference:

* [Wu, H., Xu, J., Wang, J., and Long, M. Autoformer: Decomposition Transformers
  with auto-correlation for long-term series forecasting. NeurIPS 2021.](https://arxiv.org/abs/2106.13008.)

### `TrafficL`

```python theme={null}
TrafficL(freq='H', name='traffic', n_ts=862, test_size=3508, val_size=1756, horizons=(96, 192, 336, 720))
```

This large Traffic dataset was collected by the California Department
of Transportation, it reports road hourly occupancy rates of 862 sensors,
from January 2015 to December 2016.

Reference:

* [Lai, G., Chang, W., Yang, Y., and Liu, H. Modeling Long and Short-Term Temporal
  Patterns with Deep Neural Networks. SIGIR 2018.](http://arxiv.org/abs/1703.07015)
* [Wu, H., Xu, J., Wang, J., and Long, M. Autoformer:
  Decomposition Transformers with auto-correlation for long-term series forecasting.
  NeurIPS 2021.](https://arxiv.org/abs/2106.13008)

### `ECL`

```python theme={null}
ECL(freq='15T', name='ECL', n_ts=321, n_time=26304, test_size=5260, val_size=2632, horizons=(96, 192, 336, 720))
```

The Electricity dataset reports the fifteen minute electricity
consumption (KWh) of 321 customers from 2012 to 2014.
For comparability, we aggregate it hourly.

Reference:

* [Li, S et al. Enhancing the locality and breaking the memory bottleneck of
  Transformer on time series forecasting. NeurIPS 2019.](https://arxiv.org/abs/1907.00235)

### `ETTm2`

```python theme={null}
ETTm2(freq='15T', name='ETTm2', n_ts=7, n_time=57600, test_size=11520, val_size=11520, horizons=(96, 192, 336, 720))
```

The ETTm2 dataset monitors an electricity transformer
from a region of a province of China including oil temperature
and variants of load (such as high useful load and high useless load)
from July 2016 to July 2018 at a fifteen minute frequency.

Reference:

* [Zhou, et al. Informer: Beyond Efficient Transformer for Long Sequence
  Time-Series Forecasting. AAAI 2021.](https://arxiv.org/abs/2012.07436)

### `ETTm1`

```python theme={null}
ETTm1(freq='15T', name='ETTm1', n_ts=7, n_time=57600, test_size=11520, val_size=11520, horizons=(96, 192, 336, 720))
```

The ETTm1 dataset monitors an electricity transformer
from a region of a province of China including oil temperature
and variants of load (such as high useful load and high useless load)
from July 2016 to July 2018 at a fifteen minute frequency.

### `ETTh2`

```python theme={null}
ETTh2(freq='H', name='ETTh2', n_ts=7, n_time=14400, test_size=2880, val_size=2880, horizons=(96, 192, 336, 720))
```

The ETTh2 dataset monitors an electricity transformer
from a region of a province of China including oil temperature
and variants of load (such as high useful load and high useless load)
from July 2016 to July 2018 at an hourly frequency.

### `ETTh1`

```python theme={null}
ETTh1(freq='H', name='ETTh1', n_ts=7, n_time=14400, test_size=2880, val_size=2880, horizons=(96, 192, 336, 720))
```

The ETTh1 dataset monitors an electricity transformer
from a region of a province of China including oil temperature
and variants of load (such as high useful load and high useless load)
from July 2016 to July 2018 at an hourly frequency.

### `LongHorizon2`

```python theme={null}
LongHorizon2(source_url='https://www.dropbox.com/s/rlc1qmprpvuqrsv/all_six_datasets.zip?dl=1')
```

This Long-Horizon datasets wrapper class, provides
with utility to download and wrangle the following datasets:
ETT, ECL, Exchange, Traffic, ILI and Weather.

* Each set is normalized with the train data mean and standard deviation.
* Datasets are partitioned into train, validation and test splits.
* For all datasets: 70%, 10%, and 20% of observations are train, validation, test,
  except ETT that uses 20% validation.

#### `LongHorizon2.download`

```python theme={null}
download(directory)
```

Download Long Horizon 2 Datasets.

**Parameters:**

| Name        | Type                     | Description                         | Default    |
| ----------- | ------------------------ | ----------------------------------- | ---------- |
| `directory` | <code>[str](#str)</code> | Directory path to download dataset. | *required* |

#### `LongHorizon2.load`

```python theme={null}
load(directory, group, normalize=True)
```

Downloads and long-horizon forecasting benchmark datasets.

**Parameters:**

| Name        | Type                       | Description                                                                                                     | Default           |
| ----------- | -------------------------- | --------------------------------------------------------------------------------------------------------------- | ----------------- |
| `directory` | <code>[str](#str)</code>   | Directory where data will be downloaded.                                                                        | *required*        |
| `group`     | <code>[str](#str)</code>   | Group name. Allowed groups: 'ETTh1', 'ETTh2', 'ETTm1', 'ETTm2', 'ECL', 'Exchange', 'Traffic', 'Weather', 'ILI'. | *required*        |
| `normalize` | <code>[bool](#bool)</code> | If `True` std. normalize data or not                                                                            | <code>True</code> |

**Returns:**

| Type                                        | Description                                                               |
| ------------------------------------------- | ------------------------------------------------------------------------- |
| <code>[DataFrame](#pandas.DataFrame)</code> | pd.DataFrame: Target time series with columns \['unique\_id', 'ds', 'y']. |