The method developed and described in this paper departs from the traditional time series analysis approach. The starting premise is that a time series can be broken down into a number of characteristic cases, each of which potentially holds the key for indicating the value of the subsequent observation. The case that constitutes the beginning of the forecasting horizon (the reference case) is ...

This paper presents a unified framework of stationary ARMA processes for discrete-valued time series based on Pegram’s [Pegram, G.G.S., 1980. An autoregressive model for multilag markov chains. J. Appl. Probab. 17, 350–362] mixing operator. Such a stochastic operator appears to be more flexible than the currently popular thinning operator to construct Box and Jenkins’ type stationary ARMAproces...

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-...

[3] M. Pagano, “An algorithm for fitting autoregressive schemes,” Appl. Starist., vol. 21. pp. 274-281, 1972. [4] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecusting and Cmtrd. San Francisco, CA: Holden-Day, 1970. [5] W. Gersch. “Estimation of the autoregressive parameters of a mixed autoregressive moving-average time series,” IEEE Trms. Automut. Contr., vol. AC-15, p. 583-588, 19...

This paper presents an alternative approach to time series forecasting, through use of artificial neural networks (ANNs), a relatively new concept in hydrological research. Box and Jenkins ARMAX (autoregressive moving average with exogenous inputs) models have been widely used in modeling various time series with satisfactory results. This study shows that ANNs can produce comparable, to ARMAX,...

This paper describes an optimal instrumental variable method for identifying discrete-time transfer function models of the Box-Jenkins transfer function form in the closed-loop situation. This method is based on the Refined Instrumental Variable (RIV) algorithm which, because of an appropriate choice of particular design variables, achieves minimum variance estimation of the model parameters. T...

Temperature is one of the main climatic elements that can indicate climate change as climate change seems to be one of the most important issues in the recent two decades. The aim of this research is to study temporal variation in temperature over Dibrugarh city, Assam, India during the period 1981–2010. In this article we are interested in the time series modeling of the average monthly mean t...

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSOSMN and ANF...