Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and eco...

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time...

In many intervention analysis applications, time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful, because it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying autoregressive integrated moving average (ARIMA) or fractional ARIMA model is...

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical mea...

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI reveals vehicles and their velocities using commercial imagery from a passive optical satellite-mo...

We analyze the effects on prediction intervals of fitting ARIMA models to series with stochastic trends, when the underlying components are heteroscedastic. We show that ARIMA prediction intervals may be inadequate when only the transitory component is heteroscedastic. In this case, prediction intervals based on the unobserved component models tend to the homoscedastic intervals as the predicti...

Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes i...

This paper reexamines the time series properties of the US ex post real interest rate. The estimation of the ARFIMA model using the Conditional Sum of Squares (CSS) method reveals that the ex post real interest rate can be well described using a fractionally integrated process.  2000 Elsevier Science S.A. All rights reserved.

The ability to create forecasts and discover trends is a value to almost any industry. The challenge comes in finding the right data and the appropriate tools to analyze and model such data. This paper aims to demonstrate that it may be possible to create technology forecasting models through the use of patent groups. The focus will be on applying time series modeling techniques to a collection...

Durbin’s methods for moving average (MA) and autoregressive-moving average (ARMA) estimation use the parameters of a long AR model to compute the MA parameters. Linear regression theory is applied to find the best AR order. This yields two different orders: one for the best predicting AR model and another one for the long AR model with the best parameter accuracy, as intermediate for Durbin’s e...