In this paper, the concept of long memory systems for forecasting is developed. The Pattern Modelling and Recognition System (PMRS) and Fuzzy Single Nearest Neighbour (SNN) methods are introduced as local approximation tools for forecasting. Such systems are used for matching current state of the time-series with past states to make a forecast. In the past, the PMRS system has been successfully...

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of l...

This paper surveys recent literature in the area of stock market forecasting using advanced engineering based methods like Neural Network, fractal theory, Data Mining, Hidden Markov Model and Neuro-Fuzzy system. Neural Networks and Neuro-Fuzzy systems are emerging as an effective tool to be used in the forecasting of stock market especially in machine learning techniques. Due to chaotic behavio...

The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate the prop...

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique acc...

Fuzzy time series model has been developed to either improve forecasting accuracy or reduce computation time, whereas a residul analysis in order to improve its forecasting performance is still lack of consideration. In this paper, we propose a novel Fourier method to revise the analysis of residual terms, and then we illustrate it to forecast the Japanese tourists visiting in Taiwan per year. ...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

The ever-growing use of various vehicles for transportation, on the one hand, and the statistics ofsoaring road accidents resulting from human error, on the other hand, reminds us of the necessity toconduct more extensive research on the design, manufacturing and control of driver-less intelligentvehicles. For the automatic control of an autonomous vehicle, we need its dynamic...

The manufacturing company under consideration recorded the high accident rates for last few years. These accidents cause the organization the heavy man-day loss, the production loss and heavy costs of insurance. The objective of health and safety department at the manufacturing company was to set and improve accidents prevention system. The paper presents how does the six-sigma technique will h...

Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. T...