The Markov property is a fundamental property in time series analysis and is often assumed in economic and …nancial modelling. We develop a test for the Markov property using the conditional characteristic function embedded in a frequency domain approach, which checks the implication of the Markov property in every conditional moment (if exist) and over many lags. The proposed test is applicabl...

In time series analysis using dynamic linear models, retrospective analysis involves the calculation of filtered, or smoothed, distributions for state parameters in the past. We develop and illustrate novel results that are useful in retrospective assessment of the influence of individual observations on such distributions. In particular, new and computationally simple filtering equations are d...

Due to the liberalization of countless electricity markets, load forecasting has become crucial to all public utilities for which electricity is a strategic variable. With the goal of contributing to the forecasting process inside public utilities, this paper addresses the issue of applying the Holt-Winters exponential smoothing technique and the time series analysis for forecasting the hourly ...

Interval-valued time series are interval-valued data that are collected in a chronological sequence through time. This paper adapts an approach to forecasting interval valued-time series based on Holt’s exponential smoothing method. In the adapted Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with b...

Compositional time series are formed from measurements of proportions that sum to one in each period of time. We might be interested in forecasting the proportion of home loans that have adjustable rates, the proportion of nonagricultural jobs in manufacturing, the proportion of a specific oxide in the geochemical composition of a rock, or the proportion of an election betting market choosing a...

A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer s...

the aim of this study was to determine the monthly dtr mass load of amphetamine-type compounds in poland as well as an investigation of cyclical behaviour by using time series analysis and especially trends analysis.amphetamine, methamphetamine and mdma (ecstasy) were detected in wastewater samples collected from the main wastewater treatment plant in the city of poznan using liquid chromatogra...

This paper explores the possibility of developing generalized variances for price indexes by applying nonparametric scatterplot smoothers to time series of point variance estimates. The goal here is to formulate smoothed variances which are approximately unbiased, which provide acceptable confidence interval coverage, and which are more stable than the point variance estimates. Smoothing method...