Extracting Knowledge From Time Series
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Extracting Knowledge From Time Series
Author: Boris P. Bezruchko
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-03
Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.
Progress in Artificial Intelligence: Knowledge Extraction, Multi-agent Systems, Logic Programming, and Constraint Solving
The tenth Portuguese Conference on Arti?cial Intelligence, EPIA 2001 was held in Porto and continued the tradition of previous conferences in the series. It returned to the city in which the ?rst conference took place, about 15 years ago. The conference was organized, as usual, under the auspices of the Portuguese Association for Arti?cial Intelligence (APPIA, http://www.appia.pt). EPIA maintained its international character and continued to provide a forum for p- senting and discussing researc h on di?erent aspects of Arti?cial Intelligence. To promote motivated discussions among participants, this conference streng- ened the role of the thematic workshops. These were not just satellite events, but rather formed an integral part of the conference, with joint sessions when justi?ed. This had the advantage that the work was presented to a motivated audience. This was the ?rst time that EPIA embarked on this experience and so provided us with additional challenges.
Extracting Knowledge From Opinion Mining
Data mining techniques are commonly used to extract meaningful information from the web, such as data from web documents, website usage logs, and hyperlinks. Building on this, modern organizations are focusing on running and improving their business methods and returns by using opinion mining. Extracting Knowledge From Opinion Mining is an essential resource that presents detailed information on web mining, business intelligence through opinion mining, and how to effectively use knowledge retrieved through mining operations. While highlighting relevant topics, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for information technology professionals within research or business settings, graduate and post-graduate students, as well as scholars.