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hydrological units were grouped based on spatial patterns in soil and river characteristics. soil and water characteristics are presumed to represent the hydrological units. the paraná river hydrological unit was further divided into the itajubá, mataraca, and paranapanema river sub-basins.
machine learning is a research frontier in data-intensive computing. advances in machine learning methods over the past decade have resulted in new methods that extract multiple types of spatiotemporal information directly from raw video images. many types of machine learning algorithms are useful for video analytics, but it is unclear what set of methods to use to be most effective. to the best of our knowledge, no attempts have been made to systematically compare which algorithms are most successful at analyzing video streams, and which set of characteristics most predict their performance. in this study, we designed and conducted experiments to use online video analytics to answer these questions. we collected images from facebook and youtube videos with unique identifiers and annotated their timestamps and pixel-wise action locations. we applied several classical and recently developed machine learning algorithms. we then classified them on the performance of their predictions of action occurrences.
we believe that time-series data are the most important ingredient of broad surveys of the human condition. such data can be seen to represent the present, the past, and the future. time-series data include many different kinds of information, such as temperature, photos, audio, news, etc., and collectively these data have tremendous scientific value. when aggregated in a meaningful manner, large volumes of time-series data allow for important scientific insights that can be explored using modern time-series methods. we believe that time series should be understood in a broad sense that goes beyond commonly used approaches, particularly as it has been applied to finance and economics. our definition of time series as a broad category of data that can be analyzed using modern time-series methods is a realistic description of what can be done with time-series data. in this paper, we review time series, consider their different types and motivations, and reflect on what time series analysis methods for finance and economics might be applicable to time series in general. in particular, we consider the existing solutions to the problem of modeling times series for financial forecasting.
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