Название исследуемой задачи:	Порождающие модели для прогнозирования (наборов временных рядов) в метрическом вероятностном пространстве.
Тип научной работы:	M1P/НИР/CoIS
Авторы:	Глеб Карпеев
Научный руководитель:	д.ф-м.н, В. В. Стрижов
Научный консультант:	степень, К. Яковлев

Abstract

The research is devoted to the problem of forecasting a set of time series with high covariance. Sets of time series with high variance are studied. To solve this problem, the construction of a space of pairwise distances representing the metric configuration of time series is proposed. The prediction is carried out in this space, and then the result is returned to the original space.

Research publications

Presentations at conferences on the topic of research

Software modules developed as part of the study

1. A python package mylib with all implementation here.
2. A code with all experiment visualisation here. Can use colab.