Towards Smart Data Selection From Time Series Using Statistical Methods

Abstract

Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archive.

MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB

“…Numerous value preserving data aggregation algorithms have been proposed in literature [3,8]. Among these, EveryNth, MinMax, M4 [13], and LTTB [23] are arguably the most prevalent algorithms [6,20,26].…”