Time series, periodograms, and significance

Periodograms for Multiband Astronomical Time Series

This paper introduces the multiband periodogram, a general extension of the well-known Lomb–Scargle approach for detecting periodic signals in time-domain data. In addition to advantages of the Lomb–Scargle method such as treatment of non-uniform sampling and heteroscedastic errors, the multiband periodogram significantly improves period finding for randomly sampled multiband light curves (e.g....

Periodograms & times Series

2017 1. Using information given in appendix A, identify with the periodogram the period of the main seasonal pattern in the time series beer 1. 2. Using information given in appendix B, identify with the periodogram the period of the main seasonal pattern in the time series airpass. 3. Explain R code in appendices A and B. 1 time series are taken from the R package fma, and R outputs are shown ...

Time Series Motifs Statistical Significance

Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated...

Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms

MOTIVATION Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spa...

A Significance Test for Time Series Analysis