Multi-objective optimisation in time series: Time delay agreement
Conference Paper
Overview
Research
Additional Document Info
View All
Overview
abstract
Several time delay estimates have been reported for the quasar Q0957%2b561. They come from distinct data sets and published separately. This paper presents a methodology to estimate a single time delay given several data sets by using multi-objective optimisation. We use General Regression Neural Networks (GRNN) to estimate the time delay, which is one of the most accurate time delay estimators - and faster. For the time delay agreement, we use hill-climbing search. We found that the best agreement for the time delay on Q0957%2b561 is Δ = 420 days.
Applications: time series in astronomy; Heuristic searching methods; Neural networks and applications Data sets; General regression neural network; Hill climbing; Searching methods; Time delay estimators; Artificial intelligence; Estimation; Heuristic algorithms; Multiobjective optimization; Time series; Time delay
