We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years....

Terrorist events such as suicide bombings are rare yet extremely destructive events. Responses to such events are even rarer, because they require forecasting methods for effective prevention and early detection. While many forecasting methods are available, few are designed for conflict scenarios. This paper builds on previous work in forecasting criminal behavior using spatial choice models. ...

Flood forecasting is particularly difficult and uncertain for small drainage basins. One reason for that is the absence of adequate temporal and spatial hydrological input variables for model-based flood predictions. Incorporating additional information collected by volunteers with the help of their smartphones can improve flood forecasting systems. Data collected in this way is often referred ...

A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer s...

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consi...

Fuzzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seaso...

Forecasting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, we explore the use of neural networks in forecasting the financial performance of a movie at the boxoffice before its theatrical release. In our model, we convert the forecasting problem into a classification problem—rather than f...

* Corresponding author. E-mail addresses: [email protected] (I. Pulid 1537-5110/$ – see front matter a 2008 IAgrE. doi:10.1016/j.biosystemseng.2008.09.032 Recently, Computational Neural Networks (CNNs) and fuzzy inference systems have been successfully applied to time series forecasting. In this study the performance of a hybrid methodology combining feed forward CNN, fuzzy logic and genetic algor...

Since early responses are crucial to reduce the damage from unknown Internet attacks, our first consideration while developing a defense mechanism can be on time e ciency and observing (and predicting) the change of network statuses, even at the sacrifice of accuracy. In the recent security field, it is an earnest desire that a new mechanism to predict unknown future Internet attacks needs to b...

Mobile malware is a relatively new but constantly increasing threat to information security and modern means of communication. Mobile malware evolution speedup is highly expected due to the increase of the SmartPhone and other mobile device market and malware development shift from vandalism to economic aspect. Forecasting evolution tendencies is important for development of countermeasure tech...