The advent of computer networks and the Internet has drastically altered means by which we share information & interact with each other. However, this technological advancement also created room for malevolent behaviour where individuals exploit weak points intent gaining access to confidential data, blocking activity etc. To end, intrusion detection systems (IDS) are needed filter malicious tr...

This paper investigates the effectiveness of the recently proposed Gaussian Process Dynamical Model (GPDM) on high dimensional chaotic time series. The GPDM takes a Bayesian approach to modeling high-dimensional time series data, using the Gaussian process Latent Variable model (GPLVM) for nonlinear dimensionality reduction combined with a nonlinear dynamical model in latent space. The GPDM is ...

The ubiquity of strange attractors in nature suggests that nonlinear modeling techniques can improve performance in some signal processing applications. We introduce Mixed State Markov Models (MSMMs), a refinement of Hidden Filter HMMs, and apply both to a synthetic Double Scroll time series. Forecasts by HFHMMs diverge after a few steps. Using ad hoc procedures, forecasts by MSMMs, even models...

In tidally affected coastal catchments detention pond should be provided to store flood surface water. A comparison between the full simulation approach based on the joint probability method and time series rainfall modeling via the annual maximum of pond level was undertaken to investigate the assumptions of independence between variables that are necessary in the joint probability method. The...

in this paper modeling and forecasting of revenue of taxes in fifth development plan is investigated based on a special structure of nonlinear neural networks. the time series of taxes which are studied in this research are related to total tax, direct tax, indirect tax, companies’ tax, income tax, wealth tax, and import tax. based on the correlation dimension estimation technique, the structur...

In this work, the time series modeling was used to predict the Tazareh coal mine risks. For this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. Next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. After extrusion of the trend, seasonali...

introduction: time series models are generally categorized as a data-driven method or mathematically-based method. these models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. on the other hand, a better understanding of the river flow process is vital for appropri...

Modeling and analysis of future prices has been hot topic for economic analysts in recent years. Traditionally, the complex movements in the prices are usually taken as random or stochastic process. However, they may be produced by a deterministic nonlinear process. Accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. Nonlinear ...