semvec/pypi-packages huggingface dataset:
PiecewiseLinearSegmentation 0.5
Piecewise Linear Segmentation Algorithms for Time Series
License: MIT
Keywords: Segmentation,Piece Wise Approximation,Bottomup Piece Wise,Topdown Piece Wise,Sliding Window Piece Wise

# PiecewiseLinearSegmentation

PiecewiseLinearSegmentation is a python package to perform piece wise linear representation on Time Series data. It can be used for Segmenting Time Series using Online algorithms ( sliding window, bottom-up and top-down ). Given a time series T, produce the best representation using only K segments ( a piecewise linear representation, as segmentation algorithms. )

# Installation

pip install PiecewiseLinearSegmentation

# Usage

import PiecewiseLinearSegmentation

# 1. class Sliding

PiecewiseLinearSegmentation.Sliding( max_error = max_error )

# Argument

max_error: the maximum error for any segment does not exceed some user-specified threshold.

# Method :

transform( T ) : where T is input time series, return a piecewise linear representation, a segmentation algorithms.

# 2. class Bottomup

PiecewiseLinearSegmentation.Bottomup( max_error = max_error )

# Argument

max_error: the maximum error for any segment does not exceed some user-specified threshold.

# Method :

transform( T ) : where T is input time series, return a piecewise linear representation, a segmentation algorithms.

# 3. class Topdown

PiecewiseLinearSegmentation.Topdown( max_error = max_error )

# Argument

max_error: the maximum error for any segment does not exceed some user-specified threshold.

# Method :

transform( T ) : where T is input time series, return a piecewise linear representation, a segmentation algorithms.

# References :

https://www.ics.uci.edu/~pazzani/Publications/icdm-01.pdf

Data Structures: https://github.com/NickFoubert/simple-segment
auto-orion 0.1.9
Auto ML package for anomaly detection in time series data using TadGAN
License: MIT
Requires Python: >=3.6,<3.8

# Auto-orion

## Overview
Auto-orion은 GAN 모델에 AutoML을 적용한 시계열 데이터 이상 징후 탐지용 오픈 소스 라이브러리입니다. 
본 라이브러리는 2020년 10월에 MIT 연구진에 의해 공개된 [TadGAN](https://github.com/signals-dev/Orion) 모델을 기초로 만들어졌습니다. TadGAN은 다양한 시계열 데이터 이상 징후 탐지 과제에 있어 가장 높은 성능을 보여주고 있습니다. 하지만 TadGAN은 interval, error type 등의 하이퍼파라미터에 따라 성능이 크게 달라집니다. 이에 따라 Auto-orion은 AutoML을 통해 TadGAN의 하이퍼파라미터들을 자동으로 최적화하도록 개발되었습니다. 

## Quickstart

### Installation
Anaconda 환경하에 tensorflow-gpu 1.13.1 에서만 테스트 되었습니다. 따라서 anaconda 환경에서 아래와 같이 설치하시면 됩니다.

    conda install tensorflow-gpu==1.13.1
    
다음으로 Auto-orion을 설치하시면 됩니다.

    pip install auto-orion

이제 설치가 완료되었습니다!