Short-term traffic speed forecasting based on data recorded at irregular intervals

Recent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term traffic forecasting to make forecasts more reliable, efficient, and accurate. However, most of these methods can only deal with data recorded at regular time intervals, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.published_or_final_versio

The development of hybrid intelligent systems for technical analysis based equivolume charting

This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks --Introduction, page 1

Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N) based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed

Research of fuzzy time series model based on fuzzy entropy and fuzzy clustering

时间序列预测是通过对有限个历史观测样本进行分析来建立模型，并利用模型来解释数据之间的统计规律，以期达到控制和预报目的的一门学科，在众多领域中都有非常广泛的应用。对于时间序列的建模和预测，目前已经有了许多成熟的技术和方法，但传统时间序列预测方法往往依赖大量的历史数据，而在实际问题中由于不确定性的广泛存在导致历史数据往往是不完整的、不准确的和含糊的，因而限制了传统预测模型的应用。为了解决这些问题，Song和Chissom提出了模糊时间序列的概念，其主要是在传统时间序列预测的基础上引入了模糊理论，通过建立相应的模糊逻辑关系进行预测。由于模糊时间序列在处理数据的不确定性和模糊性方面上所显示的优势，关于...Time series forecasting is modeled by limited historical observations sample, it is a technology of using the model to explain the statistical regularity of data in order to achieve the purpose of control and forecast and having a wide range of applications in many fields. For time series modeling and forecasting, there are many mature technologies and methods. The traditional time series predicti...学位：理学硕士院系专业：数学科学学院_概率论与数理统计学号：1902010115250

A Review on the Young History of the Wind Power Short-term Prediction

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out