Probabilistic Forecasting via Modern Hopfield Networks

Kashif Rasul, Pablo Vicente, Anderson Schneider, Alexander März

Keywords: Probabilistic Forecasting, Modern Hopfield Networks

TL;DR: We propose an encoder-decoder probabilistic time series forecasting model using Modern Hopfield Networks

Abstract: Hopfield networks, originally introduced as associative memory models, have shown promise in pattern recognition, optimization problems, and tabular datasets. However, their application to time series data has been limited. We introduce a temporal version that leverages the associative memory properties of the Hopfield architecture while accounting for temporal dependencies present in time series data. Our results suggest that the proposed model demonstrates competitive performance compared to state-of-the-art probabilistic forecasting models.

Submission Number: 30