We present a new method to describe time series with a highly complex time evolution. The time series is projected onto a two-dimensional phase–space plot which is quantified in terms of a multipole expansion where every data point is assigned a unit mass. The multipoles provide an efficient...

Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact...

We present a novel scaling-dependent measure for times series analysis, the progressive detrended fluctuation analysis (PDFA). Since this method progressively includes and analyzes all data points of the time series, it is suitable for on-line change-point detection: Sudden changes in the...

Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density...

In econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals. The ACD model is widely used to examine the duration of transaction volume and duration of price variations in stock markets....

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...