Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints

12/03/2019
by   Nicolas Beldiceanu, et al.
0

Given, a sequence X of n variables, a time-series constraint ctr using the Sum aggregator, and a sliding time-series constraint enforcing the constraint ctr on each sliding window of X of m consecutive variables, we describe a Θ(n) time complexity checker, as well as a Θ(n) space complexity reformulation for such sliding constraint.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/22/2020

Estimation of cluster functionals for regularly varying time series: sliding blocks estimators

Cluster indices describe extremal behaviour of stationary time series. W...
research
04/11/2023

Dangoron: Network Construction on Large-scale Time Series Data across Sliding Windows

Complex networks represent system dynamics through the interactions of a...
research
09/19/2018

Twisty Takens: A Geometric Characterization of Good Observations on Dense Trajectories

In nonlinear time series analysis and dynamical systems theory, Takens' ...
research
09/26/2016

Global Constraint Catalog, Volume II, Time-Series Constraints

First this report presents a restricted set of finite transducers used t...
research
12/02/2020

Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series

As one of the most well-known artificial feature sampler, the sliding wi...
research
05/25/2023

Sliding Window Sum Algorithms for Deep Neural Networks

Sliding window sums are widely used for string indexing, hashing and tim...
research
03/21/2023

Are uGLAD? Time will tell!

We frequently encounter multiple series that are temporally correlated i...

Please sign up or login with your details

Forgot password? Click here to reset