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Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


Artificial intelligence simulates microprocessor performance in real-time-Mis-aisa-The latest News,Tech,Industry,Environment,Low Carbon,Resource,Innovations.

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This approach is detailed in a paper presented at MICRO-54: the 54th IEEE/ACM International Symposium on MicroArchitecture.Micro-54 is one of the top conferences in the field of computer architecture and was selected as the conference's best publication. "This is a problem that needs to be studied in-depth and has traditionally relied on additional circuits to solve it," said Zhiyao Xie, lead author of the paper and a doctoral candidate in the lab of Yiran Chen, a professor of electrical and computer engineering at Duke."But our approach runs directly on microprocessors in the background, which opens up a lot of new opportunities. I think that's why people are excited about it." In modern computer processors, the computation cycle is 3 trillion times per second. Tracking the energy consumed for such a fast conversion is important to maintaining the performance and efficiency of the entire chip.