timewarpy 0.0.22

Time series processing framework and utilities for deep learning

TimeWarPY - Time Series Pre and Post Processing Methods

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

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.

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

There are a million variations to the rolling mechanism shown above including changing window sizes, increasing the number of time increments to roll, dealing with non-continuous time series, adding contextual information, dealing with time dependent meta data storage, and much more. TimeWarPY is intended to standardize these transformations to be ready for deep learning methods out of the box.

The example here: Preprocessing Univariate Data for Recurrent Neural Network Training gives a good indication how quickly TimeWarPY can get you training models with TensorFlow.