---
license: cc
task_categories:
- time-series-forecasting
language:
- en
pretty_name: WEA
size_categories:
- 10M<n<100M
---
**Description**:

The WEA dataset is derived from the WeatherBench repository and designed for Multivariable Time Series (MTS) forecasting, which is forecasting with exogenous variables, at five geographically diverse cities: London (UK), New York (US), Hong Kong (China), Cape Town (South Africa), and Singapore. It spans the period from 1979 to 2018, with a temporal resolution of 6 hours and a spatial resolution of 5.625° in both latitude and longitude. Each city is matched to its nearest grid point on the WeatherBench grid using minimal absolute distance in both axes.

**Task**: 

Time series forecasting of the 850 hPa temperature (T850) — a widely used mid-tropospheric climate indicator — at each location.

**Features**:

- Target Variable: T850 (850 hPa temperature in Kelvin) at the central grid point.
- Exogenous Variables (Total: 44):
  - Local: Z500 (500 hPa geopotential), t2m (2m temperature), u10 (10m zonal wind), v10 (10m meridional wind)
  - Spatial context: The same 5 variables (T850 + 4 exogenous) from the surrounding 8 grid points (3×3 window).


**Temporal coverage**:

- Test Set: Years 2016, 2017, 2018 — each containing ~1,460 time steps.
- Validation Set: One year preceding each test year (2015, 2016, 2017).
- Training Set: From Jan 1, 1980 to the end of the year preceding validation (e.g., up to Dec 31, 2014 for the 2015 validation year).


**Data usage**:

To load weather time series data for a specific city (e.g., London, 2016), use the following Python code:
```python
from datasets import load_dataset
dataset = load_dataset("ClaudiaShu/wea_mts", name="london_2016")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]
```


Example entry in the WEA dataset:
```python
train_data[0]
>>
{'time': '1980-01-01T00:00:00', 'z500': 51901.9375, 't850': 265.2726745605469, ..., 'v10_SE': 0.07839111983776093}
```

Alternatively, you can also define your own train/val/test split
```python
from datasets import load_dataset
london_data = load_dataset(
    "ClaudiaShu/wea_mts", 
    name="london_2016",
    split={"train": "train[:80%]", "validation": "train[80%:90%]", "test": "train[90%:]"}
)

# Access different splits
train_data = london_data["train"]
val_data = london_data["validation"]
test_data = london_data["test"]

```

**TODO**:

 - [ ]  The current dataset provides only basic train/val/test splits. In our experiments (see [our repository](https://github.com/ClaudiaShu/Sonnet)), training inputs begin earlier to ensure full coverage of the target forecasting horizon over the training period.

 - [ ]  Additionally, while we use t850 as the target variable in the paper, it is not explicitly defined here.


**Citation**:

If you find these datasets useful for your research, please consider citing the associated paper:
```
@article{shu2025sonnet,
    author    = {Yuxuan Shu and Vasileios Lampos},
    title     = {{Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting}},
    journal   = {arXiv preprint arXiv:2505.15312},
    year      = {2025}
}
```


Alternatively, we also have a paper on multivariable time series forecasting for ILI rate. You can check it out [here](https://openreview.net/pdf?id=M62P7iOT7d) and code [here](https://github.com/ClaudiaShu/DeformTime). Please also consider citing it if you find it helpful:

```
@article{shu2025deformtime,
    author    = {Yuxuan Shu and Vasileios Lampos},
    title     = {{\textsc{DeformTime}: Capturing Variable Dependencies with
                  Deformable Attention for Time Series Forecasting}},
    journal   = {Transactions on Machine Learning Research},
    issn      = {2835-8856},
    year      = {2025},
    url       = {https://openreview.net/forum?id=M62P7iOT7d},
}
```