Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barom...

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properti...

Fractionation product properties of crude distillation unit (CDU) need to be monitored and controlled through feedback mechanism. Due to inability of on-line measurement, soft sensors for product quality estimation are developed. Soft sensors for kerosene distillation end point are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery dist...

[1] A time series simulation scheme based on wavelet decomposition coupled to an autoregressive model is presented for hydroclimatic series that exhibit band-limited lowfrequency variability. Many nonlinear dynamical systems generate time series that appear to have amplitudeand frequency-modulated oscillations that may correspond to the recurrence of different solution regimes. The use of wavel...

This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms—firefly and accelerated particle swarm optimization—are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of vario...

In this paper the authors derive exact filters for the state of a doubly stochastic auto-regressive (AR) process with parameters which vary according to a nonlinear function of a Gauss–Markov process. The observations consist of a discrete-time Poisson process with rate a positive function of the Gauss–Markov process. The dimension of the sufficient statistic increases linearly with the number ...

Douglas R. Martin B-317 Dept. of Statistics University of Washington Seattle, Washington 98195 There exist large classes of time series, such as those with nonlinear moving average components, that are not well modeled by feedforward networks or linear models, but can be modeled by recurrent networks. We show that recurrent neural networks are a type of nonlinear autoregressive-moving average (...

This paper considers an exploratory modeling strategy applied to a large scale reallife problem of power load forecasting. Different model structures are considered, including Autoregressive models with eXogenous inputs (ARX), Nonlinear Autoregressive models with eXogenous inputs (NARX), both of which are also extended to incorporate residuals that follow an Autoregressive (AR) process (AR-(N)A...

A nonlinear version of the threshold autoregressive model for time series is introduced. A peculiar requirement on parameters, except possibly for the constant term, is the continuity, that seems a natural and useful assumption. This model is a special case of the general state-dependent models, where the moving-average term is dropped and a particular form for the dependence on the state is sp...