time-series-generator 0.2.8

Time Series Generator

Time Series Generator

Description

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

Instalation

pip install time-series-generator

Usage

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:

.\scripts\init-env.ps1

MacOS / Linux

./scripts/init-env.sh

Run test

Run in the terminal at project root folder:

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.

    # 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

[[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