> ## 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.

# Data Requirements

> Dataset input requirements

In this example we will go through the dataset input requirements of the
`core.NeuralForecast` class.

The `core.NeuralForecast` methods operate as global models that receive
a set of time series rather than single series. The class uses
cross-learning technique to fit flexible-shared models such as neural
networks improving its generalization capabilities as shown by the M4
international forecasting competition (Smyl 2019, Semenoglou 2021).

You can run these experiments using GPU with Google Colab.

<a href="https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/docs/getting-started/datarequirements.ipynb" target="_parent">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" />
</a>

## Long format

### Multiple time series

Store your time series in a pandas dataframe in long format, that is,
each row represents an observation for a specific series and timestamp.
Let’s see an example using the `datasetsforecast` library.

`Y_df = pd.concat( [series1, series2, ...])`

```python theme={null}
%%capture
!pip install datasetsforecast
```

```python theme={null}
import pandas as pd
from datasetsforecast.m3 import M3
```

```python theme={null}
Y_df, *_ = M3.load('./data', group='Yearly')
```

```python theme={null}
Y_df.groupby('unique_id').head(2)
```

|       | unique\_id | ds         | y       |
| ----- | ---------- | ---------- | ------- |
| 0     | Y1         | 1975-12-31 | 940.66  |
| 1     | Y1         | 1976-12-31 | 1084.86 |
| 20    | Y10        | 1975-12-31 | 2160.04 |
| 21    | Y10        | 1976-12-31 | 2553.48 |
| 40    | Y100       | 1975-12-31 | 1424.70 |
| ...   | ...        | ...        | ...     |
| 18260 | Y97        | 1976-12-31 | 1618.91 |
| 18279 | Y98        | 1975-12-31 | 1164.97 |
| 18280 | Y98        | 1976-12-31 | 1277.87 |
| 18299 | Y99        | 1975-12-31 | 1870.00 |
| 18300 | Y99        | 1976-12-31 | 1307.20 |

`Y_df` is a dataframe with three columns: `unique_id` with a unique
identifier for each time series, a column `ds` with the datestamp and a
column `y` with the values of the series.

### Single time series

If you have only one time series, you have to include the `unique_id`
column. Consider, for example, the
[AirPassengers](https://github.com/Nixtla/transfer-learning-time-series/blob/main/datasets/air_passengers.csv)
dataset.

```python theme={null}
Y_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
Y_df
```

|     | timestamp  | value |
| --- | ---------- | ----- |
| 0   | 1949-01-01 | 112   |
| 1   | 1949-02-01 | 118   |
| 2   | 1949-03-01 | 132   |
| 3   | 1949-04-01 | 129   |
| 4   | 1949-05-01 | 121   |
| ... | ...        | ...   |
| 139 | 1960-08-01 | 606   |
| 140 | 1960-09-01 | 508   |
| 141 | 1960-10-01 | 461   |
| 142 | 1960-11-01 | 390   |
| 143 | 1960-12-01 | 432   |

In this example `Y_df` only contains two columns: `timestamp`, and
`value`. To use `NeuralForecast` we have to include the `unique_id`
column and rename the previous ones.

```python theme={null}
Y_df['unique_id'] = 1. # We can add an integer as identifier
Y_df = Y_df.rename(columns={'timestamp': 'ds', 'value': 'y'})
Y_df = Y_df[['unique_id', 'ds', 'y']]
Y_df
```

|     | unique\_id | ds         | y   |
| --- | ---------- | ---------- | --- |
| 0   | 1.0        | 1949-01-01 | 112 |
| 1   | 1.0        | 1949-02-01 | 118 |
| 2   | 1.0        | 1949-03-01 | 132 |
| 3   | 1.0        | 1949-04-01 | 129 |
| 4   | 1.0        | 1949-05-01 | 121 |
| ... | ...        | ...        | ... |
| 139 | 1.0        | 1960-08-01 | 606 |
| 140 | 1.0        | 1960-09-01 | 508 |
| 141 | 1.0        | 1960-10-01 | 461 |
| 142 | 1.0        | 1960-11-01 | 390 |
| 143 | 1.0        | 1960-12-01 | 432 |

## References

* [Slawek Smyl. (2019). “A hybrid method of exponential smoothing and
  recurrent networks for time series forecasting”. International
  Journal of
  Forecasting.](https://www.sciencedirect.com/science/article/pii/S0169207019301153)
* [Artemios-Anargyros Semenoglou, Evangelos Spiliotis, Spyros
  Makridakis, and Vassilios Assimakopoulos. (2021). Investigating the
  accuracy of cross-learning time series forecasting methods”.
  International Journal of
  Forecasting.](https://www.sciencedirect.com/science/article/pii/S0169207020301850)