Scaling and SVM Zhanquan Sun, Geoffrey Fox a. Key Laboratory for Computer Network of Shandong Province, Shandong Computer Science Center, Jinan, Shandong, 250014, China b. School of Informatics and Computing, Pervasive Technology Institute, Indiana University Bloomington, Bloomington, Indiana, 47408, USA Abstract: Traffic flow forecasting is a popular research topic of Intelligent Transportatio...

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...

The apparel industry is a fashion industry with short product life and more demand uncertainty. Participants in apparel supply chain bear more risk. To lower the cost and risk for participants in apparel supply chain, a framework for apparel quick response system based on collaborative forecasting was proposed. The prototype of the apparel quick response system was provided, which included thre...

Time series forecasting is crucial in a number of domains such as production planning and energy load balancing. In these areas, forecasts are often required by non-expert users on large multi-dimensional data sets expecting short response times. However, as current traditional database systems support forecasting only in a limited and non-declarative way, it is performed outside the database s...

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of ...

Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimiza...

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...

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...

The paper addresses intelligent data processing and mining in distributed multi-agent networks. We consider a distributed traffic network represented as a multi-agent system, where each agent-vehicle autonomously forecasts its travelling time. We propose a structure for such agents and their corresponding distributed data processing and mining modules. We use distributed linear and kernelbased ...

Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this pa...