922 research outputs found
Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation
We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The LevenbergâMarquardt (LâM) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The LâM method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e InnovaciĂłn TEC2008-04920Junta de AndalucĂa P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626
Privacy-Preserving Public Information for Sequential Games
In settings with incomplete information, players can find it difficult to
coordinate to find states with good social welfare. For example, in financial
settings, if a collection of financial firms have limited information about
each other's strategies, some large number of them may choose the same
high-risk investment in hopes of high returns. While this might be acceptable
in some cases, the economy can be hurt badly if many firms make investments in
the same risky market segment and it fails. One reason why many firms might end
up choosing the same segment is that they do not have information about other
firms' investments (imperfect information may lead to `bad' game states).
Directly reporting all players' investments, however, raises confidentiality
concerns for both individuals and institutions.
In this paper, we explore whether information about the game-state can be
publicly announced in a manner that maintains the privacy of the actions of the
players, and still suffices to deter players from reaching bad game-states. We
show that in many games of interest, it is possible for players to avoid these
bad states with the help of privacy-preserving, publicly-announced information.
We model behavior of players in this imperfect information setting in two ways
-- greedy and undominated strategic behaviours, and we prove guarantees on
social welfare that certain kinds of privacy-preserving information can help
attain. Furthermore, we design a counter with improved privacy guarantees under
continual observation
Three real-space discretization techniques in electronic structure calculations
A characteristic feature of the state-of-the-art of real-space methods in
electronic structure calculations is the diversity of the techniques used in
the discretization of the relevant partial differential equations. In this
context, the main approaches include finite-difference methods, various types
of finite-elements and wavelets. This paper reports on the results of several
code development projects that approach problems related to the electronic
structure using these three different discretization methods. We review the
ideas behind these methods, give examples of their applications, and discuss
their similarities and differences.Comment: 39 pages, 10 figures, accepted to a special issue of "physica status
solidi (b) - basic solid state physics" devoted to the CECAM workshop "State
of the art developments and perspectives of real-space electronic structure
techniques in condensed matter and molecular physics". v2: Minor stylistic
and typographical changes, partly inspired by referee comment
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