Missing: forecasting | Show results with:forecasting

5 days ago · For example, if you use probabilistic forecasting, the next step can use the forecasted distribution to come up with robust decisions. Upvote

5 hours ago · The model predictions are interpreted in terms of temporal dependences and variable importances in each condition separately to shed light on the differences ...

3 days ago · Let's walk through a simple example using Pyro, a probabilistic programming library in Python. We'll use the code provided to demonstrate how a GP model evolves ...

7 days ago · Overall, the KDE method excels in interval prediction, enhancing the performance of the prediction model, which is considered a promising probabilistic ...

5 days ago · We propose a robust time-series anomaly generation algorithm using mutations and a variational recurrent autoencoder. We generate time-series anomalies from ...

Missing: series forecasting

4 days ago · In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO).

20 hours ago · In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical ...

2 days ago · The SPF asks for both point forecasts and probabilities. In particular, SPF forecasters provide probabilistic forecasts for real GDP growth and GDP deflator ...