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Time-Series Cross-Validation Module

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# Time-Series Cross-Validation

This python package aims to implement Time-Series Cross Validation Techniques.

The idea is given a training dataset, the package will split it into Train, Validation and Test sets, by means of either Forward Chaining, K-Fold or Group K-Fold.

As parameters the user can not only select the number of inputs (n_steps_input) and outputs (n_steps_forecast), but also the number of samples (n_steps_jump) to jump in the data to train.

The best way to install the package is as follows: `pip install timeseries-cv` and then use it with `import tsxv`.

<ol>
  <li>
    <a href="#Features">Features</a>
    <ul>
      <li><a href="#split-train">Split Train</a></li>
      <li><a href="#split-train-val">Split Train Val</a></li>
      <li><a href="#split-train-val-test">Split Train Val Test</a></li>
    </ul>
  </li>
  <li><a href="#Citation">Citation</a></li>
</ol>

## Features

This can be seen more intuitively using the jupyter notebook: "example.ipynb"
Below you can find an example of the usage of each function for the following Time-Series:

```
timeSeries = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26])
```

## Split Train

#### split_train
```
from tsxv.splitTrain import split_train
X, y = split_train(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

#### split_train_variableInput
```
from tsxv.splitTrain import split_train_variableInput
X, y = split_train_variableInput(timeSeries, minSamplesTrain=10, n_steps_forecast=3, n_steps_jump=3)
```

![split_train_variableInput](https://user-images.githubusercontent.com/25267873/76267051-67243f80-6261-11ea-9eba-8a25fa810b06.png)

## Split Train Val

#### split_train_val_forwardChaining
```
from tsxv.splitTrainVal import split_train_val_forwardChaining
X, y, Xcv, ycv = split_train_val_forwardChaining(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

#### split_train_val_kFold
```
from tsxv.splitTrainVal import split_train_val_kFold
X, y, Xcv, ycv = split_train_val_kFold(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

#### split_train_val_groupKFold
```
from tsxv.splitTrainVal import split_train_val_groupKFold
X, y, Xcv, ycv = split_train_val_groupKFold(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

## Split Train Val Test

#### split_train_val_test_forwardChaining

```
from tsxv.splitTrainValTest import split_train_val_test_forwardChaining
X, y, Xcv, ycv, Xtest, ytest = split_train_val_test_forwardChaining(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

#### split_train_val_test_kFold
```
from tsxv.splitTrainValTest import split_train_val_test_kFold
X, y, Xcv, ycv, Xtest, ytest = split_train_val_test_kFold(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

#### split_train_val_test_groupKFold
```
from tsxv.splitTrainValTest import split_train_val_test_groupKFold
X, y, Xcv, ycv, Xtest, ytest = split_train_val_test_groupKFold(timeSeries, n_steps_input=4, n_steps_forecast=3, n_steps_jump=2)
```

## Citation

This module was developed with co-autorship with Filipe Roberto Ramos (https://ciencia.iscte-iul.pt/authors/filipe-roberto-de-jesus-ramos/cv) for his phD thesis entitled "Data Science in the Modeling and Forecasting of Financial timeseries: from Classic methodologies to Deep Learning". Submitted in 2021 to Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal.

APA

`Ramos, F. (2021). Data Science na Modelação e Previsão de Séries Económico-financeiras: das Metodologias Clássicas ao Deep Learning. (PhD Thesis submitted, Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal).`

```
@phdthesis{FRRamos2021,
      AUTHOR = {Filipe R. Ramos},
      TITLE = {Data Science na Modelação e Previsão de Séries Económico-financeiras: das Metodologias Clássicas ao Deep Learning},
      PUBLISHER = {PhD Thesis submitted, Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal},
      YEAR =  {2021}
}
```

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