There are many various methods to forecast time series. However, there is no single forecasting method that generally outperforms any other. Consequently, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques are being proposed. These techniques combine more individual forecasting methods. In thi...

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price f...

To achieve greater sustainability of the traffic system, trend accidents in road was analysed. Injuries from are among leading factors suffering people around world. predicted to be third factor contributing human deaths. Road have decreased most countries during last decade because Decade Action for Safety 2011–2020. The main reasons behind reduction improvements construction vehicles and road...

Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybr...

In this paper, we proposed a hybrid algorithm to forecast enrolment based on fuzzy time series and genetic algorithms, the proposed algorithms presents a good forecasting result with higher accuracy rate. Historical enrolment of the University of Alabama from year 1948 to 2010 are used in this study to illustrate the forecasting process.

The scientific study article(a monograph) , presents a forecast and estimate the evolution of the market demand. Key-Words: fuzzy sets, forecasting, estimating, statistical extrapolation, market demand. 1 Problem formulation Methods for forecasting 1.1 Probabilistic Model [10] The methods operate with certain statistical parameters. The most frequently used is the mean square deviation, namely:...

The modern science is still striving to develop consciousness-based machine. The forecasting is an intuition-based or consciousness-based problem. It is an important problem for planning, decision-making and designing of an appropriate controller for the systems. The paper deals with the synergism of soft computing techniques mainly artificial neural network, fuzzy logic systems, and genetic al...

________________________________________________ AbstractPaper gives the compressed chronological work of scholars and comparison of classical and modern techniques to short term forecast the electrical load. This paper presents 10 approaches of load forecasting, their brief introduction and literature review. KeywordsLoad Forecasting, Classical Approach, Heuristic Approach, Fuzzy Logic, AI, AR...

* 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...