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 17:00–18:30
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Семестр
Осенний семестр 2025/2026 учебного года
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70 / 70