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Autoregressive models in big data problemsтезисы доклада
- Авторы: Peter Golubtsov, Denis Shchepakin
- Сборник: Montana Uncertainty Quantification. Talk Abstracts
- Тезисы
- Год издания: 2015
- Место издания: University of Montana Montana, Missoula
- Первая страница: 1
- Аннотация: The talk focuses on a method that allows one to split data into few independent chunks, analyze them separately, and combine to construct a linear autoregressive model. The method lets one gather and store the collected information in a fixed amount of memory, as well as update it on demand, which provides an opportunity to update, correct, and use the model in real-time.
- Добавил в систему: Голубцов Петр Викторович