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Auflistung nach Schlagwort "Time series analysis"

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  • Textdokument
    A Comparison of Distributed Stream Processing Systems for Time Series Analysis
    (BTW 2019 – Workshopband, 2019) Gehring, Melissa; Charfuelan, Marcela; Markl, Volker
    Given the vast number of data processing systems available today, in this paper, we aim to identify, select, and evaluate systems to determine the one that is better suited to use in conducting time series analysis. Published studies of performance are used to compare several open-source systems, and two systems are further selected for qualitative comparison and evaluation regarding the development of a time series analytics task. The main interest of this work lies in the investigation of the Ease of development. As a test scenario, a discrete Kalman filter is implemented to predict the closing price of stock market data in real-time. Basic functionality coverage is considered, and advanced functionality is evaluated using several qualitative comparison criteria.
  • Konferenzbeitrag
    A framework for capturing, statistically modeling and analyzing the evolution of software models
    (Software Engineering und Software Management 2018, 2018) Shariat Yazdi, Hamed; Angelis, Lefteris; Kehrer, Timo; Kelter, Udo
    In this work, we report about a recently developed framework for capturing, statistically modeling and analyzing the evolution of software models, published in the Journal of Systems and Software, Vol-118, Aug-2016. State-of-the-art approaches to understand the evolution of models of software systems are based on software metrics and similar static properties; the extent of the changes between revisions of a software system is expressed as differences of metrics values, and statistical analyses are based on these differences. Unfortunately, such approaches do not properly reflect the dynamic nature of changes. In contrast to this, our framework captures the changes between revisions of models in terms of both low-level (internal) and high-level (developer-visible) edit operations applied between revisions. Evolution is modeled statistically by using ARMA, GARCH and mixed ARMA-GARCH time series models. Forecasting and simulation aspects of these time series models are thoroughly assessed, and the suitability of the framework is shown by applying it to a large set of design models of real Java systems. A main motivation for, and application of, the resulting statistical models is to control the generation of realistic model histories which are intended to be used for testing model versioning tools. Further usages of the statistical models include various forecasting and simulation tasks.
  • Zeitschriftenartikel
    Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching
    (Datenbank-Spektrum: Vol. 21, No. 3, 2021) Kegel, Lars; Hartmann, Claudio; Thiele, Maik; Lehner, Wolfgang
    Processing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.