Modeling Multiple Irregularly Spaced Financial Time Series

In this paper we propose univariate volatility models for irregularly spaced financial time series by modifying the regularly spaced stochastic volatility models. We also extend this approach to propose multivariate stochastic volatility (MSV) models for multiple irregularly spaced time series by modifying the MSV model that was used with daily data. We use these proposed models for modeling intraday logarithmic returns from health sector stocks data obtained from Trade and Quotes (TAQ) database at Wharton Research Data Services (WRDS)

Gap acceptance for left turns from the major road at unsignalized intersections

This paper attempts to identify factors that may influence the gap acceptance behavior of drivers who turn left from the major road at unsignalized intersections. Drivers’ accepted and rejected gaps as well as their age and gender were collected at six unsignalized intersections with both two and four lanes on the major road, with and without the presence of a Left-Turn Lane (LTL), and with both high and low Speed Limits (SLs). Whether or not a driver accepts a given gap was considered as a binary decision and correlated logit models were used to estimate the probability of accepting a gap. Models with different factors were tested and the best model was selected by the quasi-likelihood information criterion. The gap duration, the number of rejected gaps, the mean and total time interval of the rejected gaps and the gender of the driver were all significant in explaining the variation of the gap acceptance probability, whereas the number of lanes of the major road, the presence of LTL, the SL and the driver’s age category were not. Gap acceptance probability functions were determined based on the best model, including both the factors of the number of rejected gaps and the mean time interval of the rejected gaps. As the values of these two factors increase, the probability of accepting a given gap rises up. The developed model can be further applied in practice to improve the analysis of traffic operations and capacity at unsignalized intersections. First published online: 10 Jul 201

Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data. This paper proposes a feature-based clustering approach using topological data analysis, which is a set of methods for finding topological structure in data. Persistence diagrams and landscapes are popular topological summaries that can be used to cluster time series. This paper presents a framework for selecting an optimal number of persistence landscapes, and using them as features in an unsupervised learning algorithm. This approach reduces computational cost while maintaining accuracy. The clustering approach was demonstrated to be accurate on simulated data, based on only four, three, and three features, respectively, selected in Scenarios 1–3. On real data, consisting of multiple long temperature streams from various US locations, our optimal feature selection method achieved approximately a 13 times speed-up in computing

Simultaneous prediction intervals for multiple forecasts based on Bonferroni and product-type inequalities

New simultaneous prediction intervals for multiple forecasts from ARIMA models based on the Bonferroni-type and the product-type inequalities are introduced. These prediction intervals are compared with the marginal prediction intervals used in forecasting.Autoregressive Integrated Moving Average models Bonferroni-type inequalities product-type inequalities simultaneous confidence interval estimation

Maximum likelihood estimation in vector long memory processes via EM algorithm

We present an approach for exact maximum likelihood estimation of parameters from univariate and multivariate autoregressive fractionally integrated moving average models with Gaussian errors using the Expectation Maximization (EM) algorithm. The method takes advantage of the relation between the VARFIMA(0,d,0) process and the corresponding VARFIMA(p,d,q) process in the computation of the likelihood.

Bayesian Inference for Time series with Infinite Variance Stable Innovations

This article describes the use of sampling based Bayesian inference for infinite variance stable distributions and for time series with infinite variance stable innovations. For time series, an advantage of the Bayesian approach is that it enables the simultaneous estimation of the parameters characterizing the stable law, together with the parameters of the univariate or multivariate linear ARMA model. Our approach uses a Metropolis- Hastings algorithm to generate samples from the joint posterior distribution of all the parameters and is an extension to univariate and multivariate time series processes of the approach in [Bu] for independent observations. 1. Introduction A random variable X has a stable distribution S(ff; fi; ffi; oe) if there are parameters 0 ! ff 2, \Gamma1 fi 1, oe ? 0 and \Gamma1 ! ffi ! 1 such that its characteristic function has the form ([GK]): E(e itx ) = ae exp(\Gammajoetj ff (1 \Gamma ifisign(t) tan(ßff=2) + iffit) if ff 6= 1 exp(\Gammajoetj(1 + 2if..