244 citations · 2k across the 53 of their papers we have counts for
8 papers · 1 filter
Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
Btissame El Mahtout, Florian Ziel
We propose a novel Mixture-of-Experts framework, referred to as Expert Loss Integration MoE (EliMoE), for time series forecasting. EliMoE addresses the optimization problem arising…
Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting
Souhir Ben Amor, Florian Ziel
We present a novel recurrent neural network architecture specifically designed for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operat…
Online Distributional Regression
Simon Hirsch, Jonathan Berrisch, Florian Ziel
Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain manag…
Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices
Jonathan Berrisch, Florian Ziel
This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through…
M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond
Florian Ziel
The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data faces strong overdispers…
tsrobprep - an R package for robust preprocessing of time series data
Michał Narajewski, Jens Kley-Holsteg, Florian Ziel
Data cleaning is a crucial part of every data analysis exercise. Yet, the currently available R packages do not provide fast and robust methods for cleaning and preparation of time…
CRPS Learning
Jonathan Berrisch, Florian Ziel
Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combine…
The energy distance for ensemble and scenario reduction
Florian Ziel
Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems, but also used in probabilistic f…