tsaug | A Python package for time series augmentation | Time Series Database library

by arundo Python Version: 0.2.1 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tsaug Summary

tsaug is a Python library typically used in Database, Time Series Database, Deep Learning applications. tsaug has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install tsaug' or download it from GitHub, PyPI.

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 complete documentation.

Support

Quality

Security

License

Reuse

Support

tsaug has a low active ecosystem.

It has 294 star(s) with 36 fork(s). There are 11 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 3 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tsaug is 0.2.1

Quality

tsaug has 0 bugs and 0 code smells.

Security

tsaug has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tsaug code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tsaug is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tsaug releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

tsaug saves you 937 person hours of effort in developing the same functionality from scratch.

It has 2137 lines of code, 143 functions and 21 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tsaug and discovered the below as its top functions. This is intended to give you an instant insight into tsaug implemented functionality, and help decide if they suit your requirements.

Augment X and Y
Augment the segmentation
Augments X and Y
Augment the length of the series
Augment the core
Plot the plot
Return a list of event windows

Get all kandi verified functions for this library.

tsaug Key Features

No Key Features are available at this moment for tsaug.

tsaug Examples and Code Snippets

Augmenting Time Series Data for Deep Learning

Python

Lines of Code : 26

License : Strong Copyleft (CC BY-SA 4.0)

Copy

#Convert Pandas dataframe to Numpy array and apply tsaug transformations

import numpy as np
import pandas as pd
from tsaug import TimeWarp, Crop, Quantize, Drift, Reverse

df = pd.DataFrame({"timestamp": [1, 2],"cas_pre": [687.982849, 693

Community Discussions

Trending Discussions on tsaug

Augmenting Time Series Data for Deep Learning

Usage of LSTM/GRU and Flatten throws dimensional incompatibility error

QUESTION

Augmenting Time Series Data for Deep Learning

Asked 2020-Oct-27 at 11:23

If I want to apply deep learning to the dataset from the sensors that I currently possess, I would require quite a lot data, or we may see overfitting. Unfortunately, the sensors have only been active for a month and therefore the data requires augmentation. I currently have data in the form of a dataframe that can be seen below:

...

ANSWER

Answered 2020-Oct-27 at 11:23

This is my attempt:

Source https://stackoverflow.com/questions/64382366

QUESTION

Usage of LSTM/GRU and Flatten throws dimensional incompatibility error

Asked 2020-Sep-15 at 20:26

I want to make use of a promising NN I found at towardsdatascience for my case study.

The data shapes I have are:

...

ANSWER

Answered 2020-Aug-17 at 18:14

I cannot reproduce your error, check if the following code works for you:

Source https://stackoverflow.com/questions/63455257

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tsaug

Prerequisites: Python 3.5 or later. It is recommended to install the most recent stable release of tsaug from PyPI. Alternatively, you could install from source code. This will give you the latest, but unstable, version of tsaug.

Support

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.

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