Metadata-Version: 2.1
Name: timewarpy
Version: 0.0.16
Summary: Time series processing framework and utilities for deep learning
Home-page: UNKNOWN
Author: Clay Elmore
License: BSD 3-Clause License
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE

# TimeWarPY - Time Series Pre and Post Processing Methods

[GitHub](https://github.com/celmore25/TimeWarPY)

[Full Documentation](https://timewarpy.readthedocs.io/en/latest/)

## Background and Objective

TimeWarPy is a library I created because I kept running into time-series related pre and post processing that is discussed a lot in ML literature but not standardized in a popular ML library. Most industry related forecasting methods are not well suited for real-time deep learning architectures. TimeWarPy is a stab at making these operations both fast and convenient for real-time applications through an easy to use set of core processing objections.

## Installation

TimeWarPY can be installed directly with PyPi or directly from source [here](https://github.com/celmore25/TimeWarPY)

```
pip install timewarpy
```

## Motivation

### Univariate Data

Time series data sets for deep learning generally need to be put in the visual format below. There will be a sequence in time (vector) for training and a prediction sequence in time (another vector) that is normally shorter.

![univariate_single](img/examples/univariate_single.png)

This single example is then rolled in time to generate many examples of these training and predicting sequences as shown below.

![univariate_single](img/examples/univariate_multiple.png)


