Bayesian Model Search for Nonstationary Periodic Time Series

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behaviour. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo (MCMC) algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behaviour in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces.Comment: Received 23 Oct 2018, Accepted 12 May 201

Financial time series representation using multiresolution important point retrieval method

Financial time series analysis usually conducts by determining the series important points. These important points which are the peaks and the dips indicate the affecting of some important factors or events which are available both internal factors and external factors. The peak and the dip points of the series may appear frequently in multiresolution over time. However, to manipulate financial time series, researchers usually decrease this complexity of time series in their techniques. Consequently, transfonning the time series into another easily understanding representation is usually considered as an appropriate approach. In this paper, we propose a multiresolution important point retrieval method for financial time series representation. The idea of the method is based on finding the most important points in multiresolution. These retrieved important points are recorded in each resolution. The collected important points are used to construct the TS-binary search tree. From the TS-binary search tree, the application of time series segmentation is conducted. The experimental results show that the TS-binary search tree representation for financial time series exhibits different performance in different number of cutting points, however, in the empirical results, the number of cutting points which are larger than 12 points show the better results

Optimizing the search for transiting planets in long time series

Context: Transit surveys, both ground- and space- based, have already accumulated a large number of light curves that span several years. Aims: The search for transiting planets in these long time series is computationally intensive. We wish to optimize the search for both detection and computational efficiencies. Methods: We assume that the searched systems can be well described by Keplerian orbits. We then propagate the effects of different system parameters to the detection parameters. Results: We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS one is either rather insensitive to long-period planets, or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually. Conclusions: By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the BLS parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available.Comment: 7 pages, 4 figures, 1 table. A&A accepted. Source code is available at: http://www.astro.physik.uni-goettingen.de/~avivofir

Predicting unemployment in short samples with internet job search query data

This article tests the power of a novel indicator based on job search related web queries in predicting quarterly unemployment rates in short samples. Augmenting standard time series specifications with this indicator definitely improves out-of-sample forecasting performance at nearly all in-sample interval lengths and forecast horizons, both when compared with models estimated on the same or on a much longer time series interval.Google econometrics, Forecast comparison, Keyword search, Unemployment, Time series models.

MEPSA: a flexible peak search algorithm designed for uniformly spaced time series

We present a novel algorithm aimed at identifying peaks within a uniformly sampled time series affected by uncorrelated Gaussian noise. The algorithm, called "MEPSA" (multiple excess peak search algorithm), essentially scans the time series at different timescales by comparing a given peak candidate with a variable number of adjacent bins. While this has originally been conceived for the analysis of gamma-ray burst light (GRB) curves, its usage can be readily extended to other astrophysical transient phenomena, whose activity is recorded through different surveys. We tested and validated it through simulated featureless profiles as well as simulated GRB time profiles. We showcase the algorithm's potential by comparing with the popular algorithm by Li and Fenimore, that is frequently adopted in the literature. Thanks to its high flexibility, the mask of excess patterns used by MEPSA can be tailored and optimised to the kind of data to be analysed without modifying the code. The C code is made publicly available.Comment: 9 pages, 7 figures, accepted by Astronomy and Computin

Prospects of long-time-series observations from Dome C for transit search

The detection of transiting extrasolar planets requires high-photometric quality and long-duration photometric stellar time-series. In this paper, we investigate the advantages provided by the Antarctic observing platform Dome C for planet transit detections during its long winter period, which allows for relatively long, uninterrupted time-series. Our calculations include limiting effects due to the Sun and Moon, cloud coverage and the effect of reduced photometric quality for high extinction of target fields. We compare the potential for long time-series from Dome C with a single site in Chile, a three-site low-latitude network as well as combinations of Dome C with Chile and the network, respectively. Dome C is one of the prime astronomical sites on Earth for obtaining uninterrupted long-duration observations in terms of prospects for a high observational duty cycle. The duty cycle of a project can, however, be significantly improved by integrating Dome C into a network of sites.Comment: 10 pages, 9 figures, accepted by PAS