A Python package for time series augmentation

Overview

tsaug

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tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to connect multiple augmenters into a pipeline.

See https://tsaug.readthedocs.io complete documentation.

Installation

Prerequisites: Python 3.5 or later.

It is recommended to install the most recent stable release of tsaug from PyPI.

pip install tsaug

Alternatively, you could install from source code. This will give you the latest, but unstable, version of tsaug.

git clone https://github.com/arundo/tsaug.git
cd tsaug/
git checkout develop
pip install ./

Examples

A first-time user may start with two examples:

Examples of every individual augmenter can be found here

For full references of implemented augmentation methods, please refer to References.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Please see Contributing for more details.

License

tsaug is licensed under the Apache License 2.0. See the LICENSE file for details.

Comments
  • How to cite this repo?

    How to cite this repo?

    Basically the title. I used this awesome repo and I would like to cite this repo in my paper. How to do it. If you could provide a bibtex entry that will be great

    question 
    opened by kowshikthopalli 2
  • Default _Augmentor arguments will raise an error

    Default _Augmentor arguments will raise an error

    While working on #1 I found that the default args for initializing an _Augmentor object could lead to the code trying to call None when expecting a function.

    See: https://github.com/arundo/tsaug/blob/ebf1955664991fe51f038a5cc8506f1bfc849d91/src/tsaug/augmentor.py#L5 https://github.com/arundo/tsaug/blob/ebf1955664991fe51f038a5cc8506f1bfc849d91/src/tsaug/augmentor.py#L6

    and

    https://github.com/arundo/tsaug/blob/ebf1955664991fe51f038a5cc8506f1bfc849d91/src/tsaug/augmentor.py#L47

    I know that it's not intended to be initialized without an augmenter function, function, but I was wondering if you want to explicitly prevent an error here.

    Or is something else supposed to be happening?

    bug 
    opened by roycoding 1
  • can't find the deepad python package

    can't find the deepad python package

    In the quickstart notebook https://github.com/arundo/tsaug/blob/master/docs/quickstart.ipynb from deepad.visualization import plot where can you find the deepad package to install?

    opened by xsqian 1
  • Missing function calls in documentation

    Missing function calls in documentation

    Hi!

    I noticed that documentation is actually missing few important notes.

    For instance, first example contains such snippet:

    >>> import numpy as np
    >>> X = np.load("./X.npy")
    >>> Y = np.load("./Y.npy")
    >>> from tsaug.visualization import plot
    >>> plot(X, Y)
    

    and shows a chart which suggests that it is immediately rendered after calling plot function.

    In configurations I've seen and worked on, plot function does not render any chart immediately. Instead it returns Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]. This means that we need to take first element of returned tuple and call .show() on it, so this example should rather be:

    >>> import numpy as np
    >>> X = np.load("./X.npy")
    >>> Y = np.load("./Y.npy")
    >>> from tsaug.visualization import plot
    >>> figure, _ = plot(X, Y)
    >>> figure.show()
    

    I can create a push request with such corrections if you're open for contribution

    opened by 15bubbles 0
  • Static random augmentation across multiple time series

    Static random augmentation across multiple time series

    Hello,

    I have a use case where I apply temporal augmentation with the same random anchor across multiple time series within a segmented object. I.e., I want certain augmentations to vary across objects, but remain constant within objects.

    In TimeWarp, e.g., I've added an optional keyword argument (static_rand):

        def __init__(
             self,
             n_speed_change: int = 3,
             max_speed_ratio: Union[float, Tuple[float, float], List[float]] = 3.0,
             repeats: int = 1,
             prob: float = 1.0,
             seed: Optional[int] = _default_seed,
             static_rand: Optional[bool] = False
         ):
    

    which is used by:

             if self.static_rand:                                                                                                                      
                 anchor_values = rand.uniform(low=0.0, high=1.0, size=self.n_speed_change + 1)
                 anchor_values = np.tile(anchor_values, (N, 1))
             else:
                 anchor_values = rand.uniform(
                     low=0.0, high=1.0, size=(N, self.n_speed_change + 1)
                 )
    

    Thus, instead of having N time series with different random anchor_values, I generate N time series with the same anchor value.

    I use this approach with TimeWarp and Drift. Would this be of any interest as a PR, or does it sound too specific?

    Thanks for the nice library.

    opened by jgrss 0
  • _Augmenter should be exposed properly as tsaug.Augmenter

    _Augmenter should be exposed properly as tsaug.Augmenter

    Might be related to https://github.com/arundo/tsaug/issues/1

    In the current state of the package, the _Augmenter class is an internal class that should not be used outside of the package itself... but it's also the base class for all usable classes from tsaug. This makes it very weird to type "generic" functions outside of tsaug, e.g.

    # this should not appear in a normal Python code
    from tsaug._augmenters.base import _Augmenter
    
    def apply_transformation(aug: _Augmenter):
        ...
    

    The _Augmenter class should be exposed as tsaug.Augmenter so that it can be used for proper typing outside of the tsaug package.

    help wanted 
    opened by Holt59 0
  • Equivalence in transformation names

    Equivalence in transformation names

    Hello

    I'm very interested to use and apply Tsaug library in my personal project.

    I have read the paper "Data Augmentation ofWearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks" and I'm quite confused about the name of the transformations.

    What are the equivalent in TSAUG library for the transformations Jittering, Scaling, rotation, permutation, MagWarp mentioned in this paper?

    Also, I have read the blog "https://www.arundo.com/arundo_tech_blog/tsaug-an-open-source-python-package-for-time-series-augmentation", and I didn´t find the equivalent for RandomMagnify, RandomJitter, etc.

    Could you help me with these doubts.

    Best regards

    Oscar

    question 
    opened by ogreyesp 1
  • ValueError: The numbers of series in X and Y are different.

    ValueError: The numbers of series in X and Y are different.

    The shape of X is (54, 337) and the shape of y is (54,). But I am getting error. I am using the following code

    from tsaug import TimeWarp, Crop, Quantize, Drift, Reverse
    my_augmenter = (
        TimeWarp() * 5  # random time warping 5 times in parallel
        + Crop(size=300)  # random crop subsequences with length 300
        + Quantize(n_levels=[10, 20, 30])  # random quantize to 10-, 20-, or 30- level sets
        + Drift(max_drift=(0.1, 0.5)) @ 0.8  # with 80% probability, random drift the signal up to 10% - 50%
        + Reverse() @ 0.5  # with 50% probability, reverse the sequence
    )
    data, labels = my_augmenter.augment(data, labels)
    
    question 
    opened by talhaanwarch 3
  • How to augment multi_variate time series data?

    How to augment multi_variate time series data?

    I noticed that while augmenting multi-variate time series data, augmented data is concatenated on 0 axes, instead of being added to a new axis ie third axis. Let suppose data shape is (18,1000), after augmentation it turns to be (72,1000), but i believe it should be (4,18,1000). simply reshaping data.reshape(4,18,1000) resolve the problem or not?

    question 
    opened by talhaanwarch 2
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