Identification and forecasting of the phases of the Russian economic cycle, taking into account the sectoral structure of the economy using artificial neural networks[Идентификация И Прогнозирование Фаз Российского Экономического Цикла С Учетом Отраслевой Структуры Экономики При Помощи Искусственных Нейронных Сетей]

The paper presents a method for conditional forecasting of the economic cycle taking into account industry dynamics. The predictive model includes a neural network auto-encoder and an adapted deep convolutional network of the «WaveNet» architecture. The first function block reduces the dimension of the data. The second block predicts the phase of the economic cycle of the studied industry. A neural network uses the main components of the explanatory factors as input. The proposed model can be used both as an independent and an additional method for estimating the growth rate of the industrial production index along with dynamic factor models.