Studying complex systems like ecosystems can get messy, especially when trying to predict how they interact with other big unknowns like climate change. Show In a new paper published this week (May 20) in the Proceedings of the National Academy of Sciences, researchers from the University of Wisconsin-Madison and elsewhere validate a fundamental assumption at the very heart of a popular way to predict relationships between complex variables. To model how climate changes may impact biodiversity, researchers like Jessica Blois and John W. (Jack) Williams routinely use an approach called "space-for-time substitution." The idea behind this method is to use the information in current geographic distributions of species to build a model that can predict climate-driven ecological changes in the past or future. But does it really work? "It's a necessary assumption, but it's generally untested," says lead study author Blois, a former postdoctoral fellow with Williams at UW-Madison. She is now an assistant professor at the University of California, Merced. "Yet we're using this every day when we make predictions about biodiversity going into the future with climate change." Their results should give other ecologists -- and potentially others such as economists who use similar models -- more confidence in their methods. "At these spatial and temporal scales, the space-for-time assumption does work well," says Williams, professor of geography and director of the Center for Climatic Research at the UW-Madison Nelson Institute for Environmental Studies. "Our fossil data did support the idea that you can use spatial relationships as a source of information for making these predictions for the future." Their research focus is paleoecology, the study of ancient ecosystems. By looking at fossilized pollen trapped in cores of sediment from the bottoms of lakes, the scientists reconstructed information about the plant communities present at locations across eastern North America during the past 21,000 years. If climate has influenced communities the same way across space and through time, Blois explains, then a model based on the spatial data should make the same predictions as a model based on their temporal data. And in fact, they did. The space-for-time model explained about 72 percent of the variation seen in their time data, and the remainder is likely due to other biological and environmental factors that the simplified model does not include, Blois says. Though the testing does not capture all the ways space-for-time substitutions are used in other predictive fields, she says that the results are very encouraging for questions spanning large geographic and time scales -- scales at which collecting good temporal data can be very challenging. "We found that at these broad time scales we're looking at, that space does substitute for time relatively well," Blois says. "It makes me more confident in my analyses going forward." Story Source: Materials provided by University of Wisconsin-Madison. Original written by Jill Sakai. Note: Content may be edited for style and length. The idea of predicting the future from the knowledge of the past is quite natural, even when dealing with systemswhose equations of motion are not known. This long-standing issue is revisited in the light of modern ergodic theoryof dynamical systemsand becomes particularly interesting from a pedagogical perspective due to its close link with Poincaré’s recurrence. Using such a connection, a very general result of ergodic theory—Kac’s lemma—can be used to establish the intrinsic limitations to the possibility of predicting the future from the past. In spite of a naive expectation, predictability is hindered more by the effective number of degrees of freedom of a than by the presence of chaos.If the effective number of degrees of freedom becomes large enough, whether the systemis chaotic or not, predictions turn out to be practically impossible. The discussion of these issues is illustrated with the help of the numerical studyof simple models. InfoSci-OnDemand Download Premium Research Papers Full text search our database of 169,400 titles for Forecasting to find related research papers. Learn More About Forecasting in These Related TitlesCopyright 2021. 394 pages. Organizations are facing major disruptions in tech... In Stock Copyright 2020. 333 pages. Exploring online learning through the lens of sync... In Stock Copyright 2020. 539 pages. The world is witnessing a media revolution similar... In Stock Copyright 2019. 324 pages. Community colleges serve more students than any ot... In Stock Copyright 2017. 454 pages. The majority of adult learners are looking to atta... In Stock Copyright 2016. 622 pages. While freedom of speech is a defining characterist... In Stock Copyright 2014. 465 pages. Online communities continue to evolve as more peop... In Stock Copyright 2014. 405 pages. Leaders represent a necessary part of any organiza... In Stock Copyright 2013. 457 pages. The predicted “ICT revolution” has gained increasi... In Stock Copyright 2003. 384 pages. Enterprise evolution (or electronic enterprise) is... In Stock What is the process of predicting the future based on past data?Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events.
What is the name of the process of making predictions based on past and present data through analysis of trends?Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results. Prediction is a similar, but more general term.
What is used to predict the future?Methods including water divining, astrology, numerology, fortune telling, interpretation of dreams, and many other forms of divination, have been used for millennia to attempt to predict the future.
What is data prediction?Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
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Defined as the collection of past and current information to make predictions about the future
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