time-series-generator 0.2.8
Time Series Generator
Requires Python: >=3.6

# Time Series Generator

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## Description
Emulates Teras Tensorflow TimeSeriesGenerator functionality presenting a candidate solution for the direct multi-step outputs limitation in Keras' version.

## Instalation

```terminal
pip install time-series-generator
```

## Usage

```python
import numpy as np
from time_series_generator import TimeseriesGenerator

data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])

data_gen = TimeseriesGenerator(data, targets,
                                length=10, sampling_rate=2,
                                batch_size=2)
assert len(data_gen) == 20

batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
                        np.array([[[0], [2], [4], [6], [8]],
                                [[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
                        np.array([[10], [11]]))
```

## Test

### Install requirements

#### Windows:
```powershell
.\scripts\init-env.ps1
```

#### MacOS / Linux
```bash
./scripts/init-env.sh
```

### Run test

Run in the terminal at project root folder:

```terminal
pytest -s
```

## Keras' version limitation
> A limitation of the Keras TimeseriesGenerator is that it does not directly support multi-step outputs. Specifically, it will not create the multiple steps that may be required in the target sequence.

> Nevertheless, if you prepare your target sequence to have multiple steps, it will honor and use them as the output portion of each sample. This means the onus is on you to prepare the expected output for each time step.

Brownlee, Jason

### Candidate Improvement proposed

Addition of the keyworded argument `length_output`.

```python
    # define dataset
    series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
    target = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
    # define generator
    n_input = 2
    n_output = 2
    generator = TimeseriesGenerator(series, target, length=n_input, length_output=n_output, batch_size=1)
    # print each sample
    for i in range(len(generator)):
        x, y = generator[i]
        print('%s => %s' % (x, y))
```

#### Output

```terminal
[[1 2]] => [[3 4]]
[[2 3]] => [[4 5]]
[[3 4]] => [[5 6]]
[[4 5]] => [[6 7]]
[[5 6]] => [[7 8]]
[[6 7]] => [[8 9]]
[[7 8]] => [[9 10]]
```

## References
- [Brownlee, Jason. How to Use the TimeseriesGenerator for Time Series Forecasting in Keras](https://machinelearningmastery.com/how-to-improve-deep-learning-model-robustness-by-adding-noise/)