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pro vyhledávání: '"Hahmann, Martin"'
The forecasting of time series data is an integral component for management, planning, and decision making. Following the Big Data trend, large amounts of time series data are available in many application domains. The highly dynamic and often noisy
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For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the va
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https://tud.qucosa.de/id/qucosa%3A80440
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More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is sti
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https://tud.qucosa.de/id/qucosa%3A86040
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Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. Whil
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https://tud.qucosa.de/id/qucosa%3A80476
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Autor:
Perera, Kasun S., Hahmann, Martin, Lehner, Wolfgang, Pedersen, Torben Bach, Thomsen, Christian
The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of busin
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https://tud.qucosa.de/id/qucosa%3A79865
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The role of precise forecasts in the energy domain has changed dramatically. New supply forecasting methods are developed to better address this challenge, but meaningful benchmarks are rare and time-intensive. We propose the ECAST online platform in
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https://tud.qucosa.de/id/qucosa%3A80740
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https://tud.qucosa.de/api/qucosa%3A80740/attachment/ATT-0/
Autor:
Perera, Kasun S., Hahmann, Martin, Lehner, Wolfgang, Pedersen, Torben Bach, Thomsen, Christian
Evolving customer requirements and increasing competition force business organizations to store increasing amounts of data and query them for information at any given time. Due to the current growth of data volumes, timely extraction of relevant info
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https://tud.qucosa.de/id/qucosa%3A83471
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Forecasting time series data is an integral component for management, planning and decision making. Following the Big Data trend, large amounts of time series data are available from many heterogeneous data sources in more and more applications domai
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https://tud.qucosa.de/id/qucosa%3A82105
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https://tud.qucosa.de/api/qucosa%3A82105/attachment/ATT-0/
Predicting time series is a crucial task for organizations, since decisions are often based on uncertain information. Many forecasting models are designed from a generic statistical point of view. However, each real-world application requires domain-
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https://tud.qucosa.de/id/qucosa%3A81155
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Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volume of application scenarios and da
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https://tud.qucosa.de/id/qucosa%3A72848
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