# Efficient and Robust Polylinear Analysis of Noisy Time Series

## TL;DR

This paper introduces a stochastic search method for fitting connected linear trend segments to noisy time series data, improving efficiency and robustness over traditional exhaustive approaches, with applications demonstrated on medical data.

## Contribution

It presents a novel stochastic search approach for optimal linear segment fitting in noisy time series, overcoming scalability issues of traditional grid search methods.

## Key findings

- Effective handling of severe noise in time series
- Demonstrated scalability to larger datasets
- Successful application to real medical data

## Abstract

A method is proposed to generate an optimal fit of a number of connected linear trend segments onto time-series data. To be able to efficiently handle many lines, the method employs a stochastic search procedure to determine optimal transition point locations. Traditional methods use exhaustive grid searches, which severely limit the scale of the problems for which they can be utilized. The proposed approach is tried against time series with severe noise to demonstrate its robustness, and then it is applied to real medical data as an illustrative example.