Estimating Time-Series Models From Irregularly Spaced Data

Irregularly Spaced Time Series Data with Time Scale Measurement Error

This project can be mainly divided into two sections. In the first section it attempts to model an irregularly spaced time series data where time scale is being measured with a measurement error. Modelling an irregularly spaced time series data alone is quite challenging as traditional time series techniques only capture equally/regularly spaced time series data. In addition to that, the measur...

Estimating fractal dimension of natural terrain from irregularly spaced data

Comparing non-stationary and irregularly spaced time series

In this paper we present approximate distributions for the ratio of the cumulative wavelet periodograms considering stationary and non-stationary time series generated from independent Gaussian processes. We also adapt an existing procedure to use this statistic and its approximate distribution in order to test if two regularly or irregularly spaced time series are realizations of the same gene...

Spectral Analysis of Irregularly Sampled Data with Time Series Models

Slotted resampling transforms an irregularly sampled process into an equidistant missing-data problem. Equidistant resampling inevitably causes bias, due to aliasing and the shift of the irregular observation times to an equidistant grid. Taking a slot width smaller than the resampling time can diminish the shift bias. A dedicated estimator for time series models of multiple slotted data sets w...

Time Series Analysis for Irregularly Sampled Data

Many spectral estimation methods for irregularly sampled data tend to be heavily biased at higher frequencies or fail to produce a spectrum that is positive for all frequencies. A time series spectral estimator is introduced that applies the principles of a new automatic equidistant missing data algorithm to unevenly spaced data. This time series estimator approximates the irregular data by a n...