Nov 1, 2023 · Hierarchical time series are common in several applied fields. The forecasts for these time series are required to be coherent, that is, to satisfy the con.

Apr 10, 2024 · In this context, the forecast model accounts for the interdependencies and interactions between multiple variables to make predictions. For example, predicting ...

Nov 15, 2023 · The probabilistic decomposition mechanism is designed for seasonal-trend representation that combines the historical data and forecasted data in the latent ...

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Sep 26, 2023 · () is a good example, and for general robustness testing, a third type of data set was generated (Type III). In these data sets, a fixed number of steps ...

May 13, 2024 · To generate probabilistic forecasts from arbitrary point forecasts, we directly apply the uncertainty in the underlying time series. This uncertainty usually ...

Sep 5, 2023 · The best models found in this work are ensemble approaches using multi step or a combination of multi step and dynamic predictions. Nearly all approaches offer ...

Aug 14, 2023 · Un- like other time-series models, the model SE does not model the correlation but describes a regression equation between the past and current observations ...

Jun 5, 2024 · (2022) take a probabilistic approach to robust forecasting, focusing on input perturbations and extending randomized smoothing to attain robust forecasters ...

Mar 30, 2024 · Temporal Fusion Transformer (TFT) is an attention-based transformer model for time series forecasting with a high degree of flexibility and the capacity for ...