In a competitive and dynamic market, financial institutions must forecast the proportion of mortgages that will become delinquent, default or prepay. This paper develops a novel forecasting model with nonstationary Markov chain and Grey forecasting, capable of predicting the likelihood of delinquency, default and prepayment. Home mortgage data, obtained by a major Taiwan financial institution f...

Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly and it also reduces the generation cost and increases reliability of power systems. In this work, a fuzzy logic approach for short term load fore...

Problem statement: Load forecasting plays an important task in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise short term load forecasting. Approach: This study tries to combine neural network and fuzzy logic for ne...

The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge-based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

Forecasters in firms are expected to employ mathematical techniques encoded in information systems in order to predict the future demand for a firm=s goods. In practice, many forecasters have eschewed statistical methods of forecasting and depend instead on human expertise. This resistance to the ideals and technologies of forecasting has largely been understood in the literature as a failure o...

Electrical load forecasting is well-known as one of the most important challenges in the management of electrical supply and demand and has been studied extensively. Electrical load forecasting is conducted at different time scales from short-term, medium-term and long-term load forecasting. Adaptive neuro-fuzzy inference system is a model that combines fuzzy logic and adaptive neuro system and...

This paper presents Multivariate-Factors fuzzy time series model for improving forecasting accuracy. The proposed model is based on fuzzy clustering and it employs eight main procedures to build the multivariate-factors model. The model is evaluated by studying the Egypt Wheat imports as a forecasting problem. Forecasting Egypt wheat imports depend on three factors: population size, wheat area,...

Intelligent time-series forecasting is important in several applied domains. Artificially intelligent methods for forecasting are being consistently sought. The effect of noise on time-series prediction is important to quantify for accurate forecasting with these systems. Conventionally, noise is considered obstructive to accurate forecasting. In this paper we analyse the noise impact on time-s...

Present-day operational tornado forecasting can be thought of in two parts: anticipation of tornadic potential in the storm environment, and recognition of tornadic storms once they develop. The former is a forecasting issue, while the latter is associated with warnings (or so-called nowcasting). This paper focuses on the forecasting aspect of tornadoes1, by dealing primarily with the relations...