The prediction of rainfall is essential for design and management of water resources systems. The objective of this paper is generate monthly rainfall time series and compare three linear stochastic generators for simulation of monthly... more
The prediction of rainfall is essential for design and management of water resources systems. The objective of this paper is generate monthly rainfall time series and compare three linear stochastic generators for simulation of monthly rainfall at Gharalar rain gauge stations around Urmia, Iran. Monthly rainfall data for thirty years from 1972-2002 were considered for stochastic modeling. Monthly rainfall was predicted using the Box-Jenkins, autoregressive, moving average, and ARMA models. The results indicate that the best auto-regressive model is AR (3), the best moving average model is MA (3), and the best auto-regressive moving average model is ARMA (1 , 3). Comparison of estimated and observed monthly rainfall depth for model validation from 1998-2002 showed that rainfall prediction using AR (4) with RMSE = 320.06 mm had better fitting. Keywords: Monthly Rainfall Prediction, Linear Stochastic-Base Models, Iran.
In this paper, we exactly quantify the bullwhip effect, the variance amplification in replenishment orders, for cases of stochastic demand and stochastic lead time in a simple two-stage supply chain with one supplier and one retailer. In... more
In this paper, we exactly quantify the bullwhip effect, the variance amplification in replenishment orders, for cases of stochastic demand and stochastic lead time in a simple two-stage supply chain with one supplier and one retailer. In most of the previous research, the impact of order lead time on the bullwhip effect in supply chains with pre-specified demand processes is investigated mostly for cases of deterministic lead time. In this paper, we deal with a first-order autoregressive, AR(1), demand process and investigate the behavior of a measure for the bullwhip effect with respect to autoregressive coefficient and stochastic order lead time. Extension to a mixed first-order autoregressive-moving average, ARMA(1,1), demand process is also considered.
Stereo acoustical echo cancellation is a highly challenging application in the field of acoustical signal processing. Unlike the single-channel case, conflicting requirements on the adaptive filters make this problem ill-posed in its... more
Stereo acoustical echo cancellation is a highly challenging application in the field of acoustical signal processing. Unlike the single-channel case, conflicting requirements on the adaptive filters make this problem ill-posed in its original formulation. In this contribution, it is shown how introducing an estimate of the common acoustical poles of the receiving room can lead the adaptive system to remarkable performance improvements with respect to the classical implementation. Finally, a detailed comparison between the two schemes is presented, based on a simulated acoustical environment and the use of the classical affine projection algorithm.
A texture model for synthetic aperture radar (SAR) images is presented. Specifically, a sea surface in satellite images is modeled using the two-dimensional (2-D) fractionally integrated autoregressive-moving average (FARIMA) process with... more
A texture model for synthetic aperture radar (SAR) images is presented. Specifically, a sea surface in satellite images is modeled using the two-dimensional (2-D) fractionally integrated autoregressive-moving average (FARIMA) process with a non-Gaussian white driving sequence. The FARIMA process is an ARMA type model which is asymptotically self-similar. It captures the long-range as well as short-range spatial dependence structure of an image with a small number of parameters. To estimate these parameters, an efficient estimation procedure based on a spectral fit is presented. Real-life ocean surveillance radar images collected by the RADARSAT sensor are used to evaluate the practicality of this FARIMA approach. Using the radial power spectral density, the new model is shown to provide a more accurate description of the SAR images than the conventional moving-average (MA), autoregressive (AR), and fractionally differenced (FD) models.
Abstract-In this paper we present improved signal processing techniques which can be used to reduce the discrete as well as spatially spread clutter in radar systems through space-time processing. Several techniques are proposed for... more
Abstract-In this paper we present improved signal processing techniques which can be used to reduce the discrete as well as spatially spread clutter in radar systems through space-time processing. Several techniques are proposed for clutter reduction, most of them model ...
Stock price forecasting has attracted tremendous attention of researchers over the past several decades. Many techniques thus have been proposed so far to deal with the problem. This paper presents an application of a computational... more
Stock price forecasting has attracted tremendous attention of researchers over the past several decades. Many techniques thus have been proposed so far to deal with the problem. This paper presents an application of a computational intelligence technique - a fuzzy inference system, namely Standard Additive Model (SAM), for predicting stock price time series data. The modelling and learning power of the SAM have been benefited to build the model that is capable of prediction functionalities. Experimental results have demonstrated that the proposed approach outperforms the traditional Auto Regressive Moving Average (ARMA) model in terms of the forecasting performance.
The recently developed method of pure-order recursive ladder algorithms (PORLA) is extended to facilitate the identification of autoregressive moving-average (ARMA) models. Since the time recursion in this method is limited in the... more
The recently developed method of pure-order recursive ladder algorithms (PORLA) is extended to facilitate the identification of autoregressive moving-average (ARMA) models. Since the time recursion in this method is limited in the calculation of the input data covariance matrix, roundoff errors cannot propagate in time in higher stages of the pure-order recursively constructed ladder form. Thus, the superior least-squares tracking and fast start-up capability of the proposed algorithms is not corrupted by roundoff error. Furthermore, the algorithms allow the use of higher-order recursive windows on the data (e.g., recursive Hanning), which again significantly improves the tracking as well as the steady-state behavior. A computer program, an instructive example for implementation of the method on a massively parallel processor, and several experimental results which confirm the superior properties of the PORLA method over conventional techniques are shown
In this article we have used the ARMA (autoregressive moving average process) and persistence models to predict the hourly average wind speed up to 10 h in advance. In order to adjust the time series to the ARMA models, it has been... more
In this article we have used the ARMA (autoregressive moving average process) and persistence models to predict the hourly average wind speed up to 10 h in advance. In order to adjust the time series to the ARMA models, it has been necessary to carry out their transformation and standardization, given the non-Gaussian nature of the hourly wind speed distribution and the non-stationary nature of its daily evolution. In order to avoid seasonality problems we have adjusted a different model to each calendar month. The study expands to five locations with different topographic characteristics and to nine years. It has been proven that the transformation and standardization of the original series allow the use of ARMA models and these behave significantly better in the forecast than the persistence model, especially in the longer-term forecasts. When the acceptable RMSE (root mean square error) in the forecast is limited to 1.5 m/s, the models are only valid in the short term.
In recent years the ecological conditions in areas of important wetlands have markedly changed. One of the areas is also Kláštorské Lúky the national natural reservation wetland, which is situated in the Stráovské mountains in the... more
In recent years the ecological conditions in areas of important wetlands have markedly changed. One of the areas is also Kláštorské Lúky the national natural reservation wetland, which is situated in the Stráovské mountains in the northwest of the Slovak Republic. This study deals with the modeling and forecasting of discharge and rainfall time series in the area of the
Weather data is vital to the success of weather risk management. Without relevant, high quality data, pricing and management of weather risk would be unfeasible. The motivation for this research derives from the necessity of... more
Weather data is vital to the success of weather risk management. Without relevant, high quality data, pricing and management of weather risk would be unfeasible. The motivation for this research derives from the necessity of 'clean' weather temperature data, as most weather derivatives pricing methodologies rely heavily on them. The methodology adopted is to discard certain observed values and treat
Currently many applications require tracking moving objects, and that in- formation is used to plan the path of motion or change according to the position of a visual target (1) (2). Computer vision systems can predict the motion of... more
Currently many applications require tracking moving objects, and that in- formation is used to plan the path of motion or change according to the position of a visual target (1) (2). Computer vision systems can predict the motion of objects if the movement behavior is analyzed over time, ie, it is possible to find out future val- ues based on
In this paper, the application of ambient intelligence computing techniques in the prediction of occupant behaviours is addressed. It is aimed to deliver a wellbeing monitoring and assistive environment to support elderly lives... more
In this paper, the application of ambient intelligence computing techniques in the prediction of occupant behaviours is addressed. It is aimed to deliver a wellbeing monitoring and assistive environment to support elderly lives independently, in control of their day to day activities. A wireless sensor network is constructed to collect the required occupancy data. Individual sensory data are combined to form an occupancy time series. In this paper different techniques in time series prediction are investigated. The prediction techniques include an Evolving Fuzzy Predictor (EFP) model along with Auto Regressive Moving Average (ARMA) model, Adaptive-Network-based Fuzzy Inference System (ANFIS), as well as Transductive Neuro-Fuzzy Inference model with Weighted data normalization (TWNFI). These prediction techniques are used to predict the occupancy time series representing anticipated occupancy of different areas of the environment, and the results are compared. Experimental results ar...
In this paper we analyse the repeated time series model where the fundamental component follows a ARMA process. In the model, the error variance as well as the number of repetition are allowed to change over time. It is shown that the... more
In this paper we analyse the repeated time series model where the fundamental component follows a ARMA process. In the model, the error variance as well as the number of repetition are allowed to change over time. It is shown that the model is identified. The maximum likelihood estimator is derived using the Kalman filter technique. The model considered in
