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T-DAM is a database management system designed specifically for economic time-series data. It was designed to provide users with an interactive on line capability of accessing files or subsets of files within a centralized data ba...
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T-DAM is a database management system designed specifically for economic time-series data. It was designed to provide users with an interactive on line capability of accessing files or subsets of files within a centralized data base. This report contains documentation on how to load, manage, and use this system. Specific instructions in management, limitations, policy, and some sample tasks are presented.
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The Multiple Diode Array (MDA) was used to obtain a data set on granulation that is suitable for image restoration and correlation tracking studies as well as for studies of the time variation of granules and filigree. The (MDA) o...
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The Multiple Diode Array (MDA) was used to obtain a data set on granulation that is suitable for image restoration and correlation tracking studies as well as for studies of the time variation of granules and filigree. The (MDA) operates a thin RCA 320 x 512 CCD array so that individual pixels and areas may be selectively digitized. The time series reported here uses a raster of 130 x 132 pixels with each pixel equal to 0.11 arc seconds (about half the telescope resolution) for a total field of 14.3 arc seconds. The exposures are 4 milliseconds and the interval between frames s 0.55 seconds. The July 17, 1983 observing run is especially good in that the seeing was excellent and the run lasted over one hour. The time series was taken in order to develop correlation-tracking techniques, frame-sharpness selection criteria and methods for destretching the scene. The series can also be used for studying granulation and filigree.
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This bibliography, together with a recent report by Parzen entitled 'Time Series Analysis Methods and Applications: Increased Interdisciplinary Interaction Could Stimulate Research Breakthroughs', have been prepared to help planni...
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This bibliography, together with a recent report by Parzen entitled 'Time Series Analysis Methods and Applications: Increased Interdisciplinary Interaction Could Stimulate Research Breakthroughs', have been prepared to help planning for future directions of research and education in statistical time series analysis methods and applications. This bibliography provides a list of books available in English, and reproduces from each book its title page and table of contents. It also includes prefaces and references from many books. (Author)
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Detection of changes in the resonant frequencies and mode shapes of a system is a fundamental problem in dynamics. This paper describes a time series method of detecting and quantifying changes in these parameters for a ten degree...
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Detection of changes in the resonant frequencies and mode shapes of a system is a fundamental problem in dynamics. This paper describes a time series method of detecting and quantifying changes in these parameters for a ten degree-of-freedom bilinear system excited by narrow band random noise. The method partitions the state space and computes mode frequencies and mode shapes for each region. Different regions of the space may exhibit different mode shapes, allowing diagnosis of stiffness changes at structural discontinuities. The method is useful for detecting changes in the properties of joints in mechanical systems or for detection of damage as the properties of a structure change during use.
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A particular form of filtered Poisson process is suggested for modeling time series data tht contain episodes, i.e. large, exponentially decaying excursions away from baseline values of the series. The properties of this process a...
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A particular form of filtered Poisson process is suggested for modeling time series data tht contain episodes, i.e. large, exponentially decaying excursions away from baseline values of the series. The properties of this process are derived and the principle of conditional least squares estimation is used to obtain consistent, asymptotically normal estimators of the average excursion heights and the decay rate parameter. The method is illustrated using hormone levels data. (Author)
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Temperature control of an electrical furnace is generally accomplished by means of a servo mechanism. For simple laboratory experiments, however, it may be desirable to control temperature by manual adjustment of power and thus av...
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Temperature control of an electrical furnace is generally accomplished by means of a servo mechanism. For simple laboratory experiments, however, it may be desirable to control temperature by manual adjustment of power and thus avoid the expense of building a servo. A procedure is offered here for producing a linear temperature rise in an electrical furnace by a time-dependent power function, Q(t). This function is a correspondence of power needed in the heating coil (to produce the desired rate of temperature rise) vs time. A computer analyzes the characteristics of the furnace, calculates the function, and tabulates it. By an iteration of the procedure, the deviation of the temperature-time curve from linearity can be decreased from about 15% to less than 1%. (Author)
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We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmi...
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We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. In our paper we show how time series generated by sensors can be captured and stored in a database system (archive). Sensors compress time series instead of sending them in raw form. We propose an optimal on-line algorithm for constructing a piecewise constant approximation (PCA) of a time series which guarantees that the compressed representation satisfies an error bound on the L(infinity) distance. in addition to the capture task, we often want to estimate the values of a time series ahead of time, e.g., to answer real-time queries. To achieve this, sensors may fit predictive models on observed data, sending parameters of these models to the archive. We exploit the interplay between prediction and compression in a unified framework that avoids duplicating effort and leads to reduced communication.
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A periodically integrated autoregressive process for a time series which is observed s times per year is a process for which there exist s-1 co-integration relations between the annual series containing the s seasonal observations...
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A periodically integrated autoregressive process for a time series which is observed s times per year is a process for which there exist s-1 co-integration relations between the annual series containing the s seasonal observations. This means that there is a single unit root in the vector autoregression for these annual series. In the paper it is shown that temporally aggregating such a process dose not effect the presence of this unit root, i.e. the aggregated series is also periodically integrated.
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