16 citations · 29 across the 8 of their papers we have counts for
6 papers · 1 filter
The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference
Pablo Montero-Manso, Marcel Scharth
We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining. Analysts specify a generative world, a d…
FRANS: Automatic Feature Extraction for Time Series Forecasting
Alexey Chernikov, Chang Wei Tan, Pablo Montero-Manso +1
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to ach…
A Look at the Evaluation Setup of the M5 Forecasting Competition
Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir +1
Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision…
Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +2
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are t…
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecasting
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +1
Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, re…
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
Pablo Montero-Manso, Rob J Hyndman
Forecasting groups of time series is of increasing practical importance, e.g. forecasting the demand for multiple products offered by a retailer or server loads within a data cente…