This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined...

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined...

Prediction of solar irradiance has great significance to photovoltaic power forecasting and the scheduling plan of power generation. Aim at unsatisfactory prediction accuracy of traditional forecasting methods, this paper presents an approach to predict solar irradiance of photovoltaic power station based on wavelet decomposition and extreme learning machine. With historical irradiance sequence...

Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-ofthe-art learning model...

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding ...

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding ...

Electricity price predictions have become a major discussion on competitive market under deregulated power system. But, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. In this paper, a new forecast strategy based on the iterative neural network is proposed for Day-ahead price...

In this paper‎, ‎the impacts of premium bounds of put option contracts on the operation of put option and day-ahead electricity markets are studied‎. ‎To this end‎, ‎first a comprehensive equilibrium model for a joint put option and day-ahead markets is presented‎. ‎Interaction between put option and day-ahead markets‎, ‎uncertainty in fuel price, impact of premium bounds, and elasticity of con...

This work underlies the poster presented at the XVIII Workshop on Quantitative Finance (QFW2017) in Milano on January 25-27, 2017. We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead ...

Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objec...