LSTperiod software: spectral analysis of multiple irregularly sampled time series

George Caminha-Maciel, Marcia Ernesto


  1. Irregularly sampled time series are common in several different areas, such as astronomy,meteorology, biology, oceanography, cyclostratigraphy, and others. The periodogram is aprimary tool to extract meaningful information from irregularly spaced and noisy time series. It  is an element of decision theory, meaning the periodogram usually transforms the data, and itsordinates are subsequently submitted to a statistical test compared to a population originatingfrom a known stochastic model (white Gaussian noise). If some ordinate f0 (usually a localmaximum, a peak) fails in this test, we declare that it is a ‘periodicity’ at a frequency f0. Besidesits full usage, this method until now suffer from numerous theoretical difficulties in adapting toreal case situations and shows lack of usefulness for very poorly sampled and high noise cases.All of it implies low usefulness for applying in most sedimentary sequences at our disposalnowadays.

  2. The LSTperiod is an application, written in Matlab, conceived to perform spectralanalysis of multiple irregularly sampled time series. It combines information from Lomb-Scargleperiodogram estimates over different time series sampling the same phenomenon, enabling therecovering of signals from very poorly sampled and noisy time series. The software comprises aset of four Graphical User Interfaces (GUIs) that allow the user to:

  3. (1) Have broad choices of the frequency-domain range and density for spectral  estimation;

  4. (2) Select possible spectral features (i.e., pick "T") for testing as a model[A∗sin(2π t −θ)] Tthrough the visualization of several goodness-of-fit statistics;

  5. (3) Visualize the fitting residuals in the time domain, for each time series, for the chosensinusoidal model.These tools help the user to identify and analyze any suspected feature in the estimatedspectra through its related linear system responses. All estimated parameter can be saved on  worksheets and the visualizations in several different figure formats. We illustrate the use of the software with a set of Ocean Drilling Program (ODP) data series that show long-period Milankovitch-related spectral features and demonstrate its performance using synthetic time series.


Paleoceanography and paleoclimatology; Paleoclimate; Inverse methods; Statistical analysis; Methods - General or miscellaneous

Full Text:



We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it (Read more).

Published by INGV, Istituto Nazionale di Geofisica e Vulcanologia - ISSN: 2037-416X