The monitoring of statistical network properties could be useful for the short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the 10 earthquake catalogue of INGV for the case of the L’Aquila (Italy) mainshock (Mw = 6.3) of 6th April 2009, we provide evidence that network measures, both global (e.g. ...

In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism. The data and forecasts are organized in a hierarchy based on disaggregating the data for different geographical regions and for different purposes of travel. We consider five approaches to hierarchical forecasting: two variations of the top-down approa...

the difficulty of determining intrinsic value of stock prices have led many people to use technical analysis in order to forecast stock prices in the future. to predict the stock price we need to determine the generating process of stock prices. in recent years many time - series methods have been used for forecasting purposes. one of these methods is the rescaled range analysis (ris). in the a...

The development and deployment of an effective wind speed forecasting technology can improve the stability safety power systems with significant penetration. However, due to wind’s unpredictable unstable qualities, accurate is extremely challenging. Several algorithms were proposed for this purpose level reliability. A common method making predictions based on time series data long short-term m...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...

Forecasting always plays an important role in business, technology and many others and it helps organizations to increase profits, reduce lost sales and more eff icient production planning. A parallel algorithm for forecasting reported recently on OTIS-Mesh[9]. This parallel algorithm requires 5( – 1) electronic steps and 4 optical steps. In this paper we present an improved parallel algorithm ...

Load forecasting is important for safe and cost-effective operation of recent power utilities. It helps in taking many decisions regarding energy purchasing and generation, maintenance, etc. Further, load forecasting provides information which is able to be used for energy interchange with other utilities. Over the years, a number of methods have been proposed for load forecasting. This paper f...

Review and classification of electric load forecasting (LF) techniques based on artificial neural networks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANN oriented applications for forecasting are given in the literature. These are classified into five groups: (1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs in...