Jan 16, 2024 · Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly ...

Aug 10, 2023 · Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time ...

Feb 11, 2024 · Lag-Llama demonstrates strong performance in time series forecasting, comparing favorably with supervised baselines across unseen datasets in both zero-shot and ...

Feb 8, 2024 · We present Lag-Llama, a foundation model for univariate probabilistic time series forecasting based on a simple decoder-only transformer architecture that uses ...

Aug 11, 2023 · Focused on ease of use and robustness, AutoGluon–TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code.

Jun 6, 2024 · In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, ...

Nov 30, 2023 · When outliers are present in a dataset, they can disrupt the calculated summary statistics, such as the mean and standard deviation, leading the model to ...

Apr 4, 2024 · Probabilistic models are based on the propagation of uncertainty through different levels of hierarchy. This enables both a top-down and bottoms-up approach to ...

May 26, 2024 · Application: General-purpose time series forecasting with a focus on robustness and ease of use. Strengths: Robust probabilistic forecasting, easy to use with ...

Mar 19, 2024 · This is where Monte Carlo Simulation (MCS) steps in, offering a powerful approach to generate probabilistic forecasts and quantify uncertainty. In this blog ...