Like many other economic time series, US aggregate retail sales have strong trend and seasonal patterns. How to best model and forecast these patterns has been a long-standing issue in time-series analysis. This article compares arti"cial neural networks and traditional methods including Winters exponential smoothing, Box}Jenkins ARIMA model, and multivariate regression. The results indicate th...

In the previous paper (Pupeikis, 1998), the problem of recursive estimation of the state of linear dynamic systems, described by an autoregressive model (AR), in the presence of timevarying outliers in observations to be processed has been considered. An approach to the robust recursive state estimation has been obtained and proved by estimating the real chemical process (Box and Jenkins, 1970)...

For many years, various methods for the identification and estimation of parameters in linear, discrete-time transfer functions have been available and implemented in widely available Toolboxes for Matlab. This paper considers a unified Refined Instrumental Variable (RIV) approach to the estimation of discrete and continuous-time transfer functions characterized by a unified operator that can b...

System identification is the process of creating models of dynamic process from inputoutput signals. The aim of system identification can be identified as “ to find a model with adjustable parameters and then to adjust them so that the predicted output matches the measured output”. This paper presents a method of modeling and simulating with system identification to achieve the maximum fitness ...

Particulate air pollution is associated with a range of effects on human health, including effects on the respiratory and cardiovascular systems, asthma and mortality. Hence, the development of an efficient forecasting and early warning system for providing air quality information towards the citizen becomes an obvious and imperative need. The objective of this work was to investigate that fore...