The goal of this course is to discuss a selection of seasonal time series models for economic and financial forecasting with R. The first part starts by introducing seasonal and periodic time series models for macroeconomic variables, and then progresses to more advanced seasonal models like Generalized Additive Models (GAM), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) models, and the Monash Electricity Forecasting Model (MEFM), which are useful for dealing with electricity/gasoline/temperature data. Some of these models can be used for applications in artificial intelligence.
Курс по искусственному интеллекту
Факультет
Московская школа экономики
Преподаватели
Где
Ленинские горы д.1 стр. 61, ауд. 317
Когда
Среда 15:10–16:40
Нагрузка:
Аудиторная [ч]: 24
Самостоятельная [ч]: 12
Семестр
Осенний семестр 2026/2027 учебного года
Записалось / всего мест
0 / 70