Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, such as GNP and its components, price indices, sales, and stock returns are observed over time. In addition to being interested in the contemporaneous relationships among such variables, we are often concerned with relationships between their current and past values, that is, relationships over time.

Time Series Analysis

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Demonstrates that Ethernet LAN traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal-like behavior, that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks, and that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it. These conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. The authors also present traffic models based on self-similar stochastic processes that provide simple, accurate, and realistic descriptions of traffic scenarios expected during B-ISDN deployment. >

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On the self-similar nature of Ethernet traffic (extended version)

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

Part 1 Scope and background material: 1. Environmetrics, Science and Decision Making. 2. Basic Statistical Concepts. Part 2 Linear nonseasonal models: 3. Stationary Nonseasonal Models. 4. Nonstationary Nonseasonal Models. Part 3 Model construction: 5. Model Identification. 6. Parameter Estimation. 7. Diagnostic Checking. Part 4 Forecasting and simulation: 8. Forecasting with Nonseasonal Models. 9. Simulating with Nonseasonal Models. Part 5 Long memory modelling: 10. The Hurst Phenomenon and Fractional Gaussian Noise. It. Fractional Autoregressive-Moving Average Models. Part 6 Seasonal models: 12. Seasonal Autoregressive Integrated Moving Average Models. 13. Deseasonalized Models. 14. Periodic Models. 15. Forecasting with Seasonal Models. Part 7 Multiple input-single output models: 16. Causality. 17. Constructing Transfer Function-Noise Models. 18. Forecasting with Transfer Function-Noise Models. Part 8 Intervention analysis: 19. Building Intervention Models. Part 9 Multiple input-multiple output models: 20. General Multivariate Autoregressive Moving Average Models. 21. Contemporaneous Autoregressive-Moving Average Models. Part 10 Handling messy environmental data: 22. Exploratory Data Analysis and Intervention Modelling in Confirmatory Data Analysis. 23. Nonparametric Tests for Trend Detection. 24. Regression Analysis and Trend Assessment.

Time series modelling of water resources and environmental systems

Previous research related to the controversial Hurst phenomenon is reviewed and evaluated. Because of the inherent statistical properties of the rescaled adjusted range (RAR) statistic it is suggested that research primarily be devoted to this statistic rather than to the various definitions of the Hurst coefficient. Simulation studies reveal that for independently distributed random variables the RAR does not significantly depend on the underlying distribution of the random variables but is a function of the sample size. For modeling correlated data the statistical attributes of a discrete fractional Gaussian noise (FGN) process are studied and also improved. An efficient maximum likelihood estimation technique is developed for the FGN model, and it is shown how the residuals of the fitted model can be calculated and then subjected to diagnostic checks. An exact simulation procedure is developed for simulating FGN in such a way that synthetic traces from the model lie outside the Brownian domain. The Akaike information criterion (AIC) is suggested as a method for choosing between a FGN and a Box-Jenkins model. For the six annual river flow series that are considered the AIC selects the best Box-Jenkins model in preference to the FGN process for each data set. Because Box-Jenkins models can be shown to preserve the historical RAR, in many practical applications it may be advantageous to use a Box-Jenkins model instead of a FGN process.

Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon

The graph model for conflicts

Many complex problems may be modeled as conflicts, but until now a comprehensive technique for analyzing real world conflicts has not been available. A method is presented which permits the rapid assessment of complex conflict situations for the purpose of finding resolutions to a conflict, determining correct strategies, and enhancing one's understanding of the problem. The new technique is presented through a step by step example, and following this the mathematical basis for the analysis algorithm is developed. Finally, extensions to the procedure are presented through the analysis of the fall of France in 1940, an international water allocation conflict, and the Garrison Diversion Unit (GDU) irrigation project in North Dakota, U.S.A.

Solving Complex Conflicts

A comprehensive decision support system, GMCR II, is developed for the systematic study of real-world interactive decision problems. Model formulation is presented in Part I, and analysis and output interpretation in Part II. GMCR II is based upon existing and new research developments of the graph model for conflict resolution. In Part I, specially designed data structures and corresponding algorithms are implemented for generating and representing possible states, removing infeasible states, coalescing indistinguishable states, and specifying and storing allowable state transitions. Algorithms implementing different approaches to the elicitation of preferences over states enable GMCR II to construct and manage an efficient, flexible, and complete graph model of a strategic conflict.

Keith W. Hipel

Papers

Time series modelling of water resources and environmental systems

Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon

The graph model for conflicts

Solving Complex Conflicts

A decision support system for interactive decision making-Part I: model formulation