This paper presents a new application of Hidden Markov Model (HMM) as a forecasting tool for the prediction of the currency exchange rate between the US dollar and the euro. The results obtained show that the difference between price gaps which consists open, high, and low price can be selected to produce the best model parameter of Hidden Markov Model. Three model parameters based on Akaike In...

The aim of statistical modeling is to identify the model that most closely approximates the underlying process. Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation. In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid cens...

Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical informati...

The precise interpreting of RF data starts from retrieving or knowing the exact time instant at which moment sender is turned on, this challenge implies two important issues; prevent manipulating redundant information such as unavoidable background noise speed up processing and other issue to study behavior that sender. A method has been developed automatically catch onset in transient Bluetoot...

Akaike’s criterion is often used to test composite hypotheses; for example to determine the order of a priori unknown Auto-Regressive and/or Moving Average models. Objections are formulated against Akaike’s criterion and some modifications are proposed. The application of the theory leads to a general technique for AR-model order estimation based on testing pairs of composite hypotheses. This t...

AbstructThe model selection problem for sinusoidal signals has often been addressed by employing the Akaike information criterion (AIC) and the minimum description length principle (MDL). The popularity of these criteria partly stems from the intrinsically simple means by which they can be implemented. They can, however, produce misleading results if they are not carefully used. The AIC and MDL...

Partial least squares (PLS) regression is a powerful and frequently applied technique in multivariate statistical process control when the process variables are highly correlated. Selection of the number of latent variables to build a representative model is an important issue. A metric frequently used by chemometricians for the determination of the number of latent variables is that of Wold’s ...

During the last decades, the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models has increased constantly. Because the ITC are derived under the strong assumption that the measured signals are stationary, it is not straightforward to employ them in combination with the forgetting factor least-squares algorithms. In the previous literature, the attem...

hydrological drought refers to a persistently low discharge and volume of water in streams and reservoirs, lasting months or years. hydrological drought is a natural phenomenon, but it may be exacerbated by human activities. hydrological droughts are usually related to meteorological droughts, and their recurrence interval varies accordingly. this study pursues to identify a stochastic model (o...