forecasting algorithms like ARIMA, SARIMA, Exponential Smoothing, and Facebook Prophet. The paper analyses the effectiveness of …

across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we …

forecasting solutions. In this context, the paper develops new forecasting methods for an old problem by employing 13 machine …

forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions … the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six …

-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature … for forecasting consumption developments. …

-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature … for forecasting consumption developments. …

Allocation algorithm. For the forecasting experiment, we select 10 sign-adjusted topics that show strong correlations with GDP … information beyond professional forecasts. In an out-of-sample forecasting experiment, we also find that combining Dynamic Factor … solely on hard data across all forecasting horizons, with the greatest improvements seen in nowcasts. These results …

. However, this paper argues that, from the perspective of time series, ML prediction is merely a one-step static forecasting …. Our results show that: First, for one-step forecasting, all models exhibit good predictability; among them, ML methods do … forecasters when it comes to multistep forecasting; except the deep learning method known as long short-term memory (LSTM), other …