# Foreclassing: A new machine learning perspective on human decision making with temporal data

arXiv: https://arxiv.org/abs/2503.04956 · HTML: https://mlsanity.com/paper/2503.04956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities](https://mlsanity.com/paper/2307.13565/) (2023)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)

## Abstract (arXiv)

Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and uncertainty about future outcomes, and then make a decision. In this paper, we propose a new machine learning problem, which we call Foreclassing, which addresses settings in which the aim is to automate human involvement in such decision-making processes. Our aim is to develop a unified end-to-end model that takes a time series as input, produces a forecast, accounts for its predictive uncertainty, and makes a downstream classification decision, enabling models to support or automate such temporal decision-making tasks. Related problems arise across a range of applications, yet the literature lacks both a unified methodology and a formal problem statement. By formalizing the task, we aim to stimulate research on such models and encourage cross-domain collaboration. To solve the Foreclassing problem, we propose a deep Bayesian neural network, ForeClassNet. As part of this framework, we introduce a new type of neural network layer, Boltzmann convolutions, which enable probabilistic learning of kernel sizes in convolutional layers. We evaluate the Foreclassing framework against standard time series classification methods and demonstrate the efficacy of ForeClassNet on real-world Foreclassing datasets from the weather, energy, and finance domains, achieving superior performance relative to state-of-the-art time series classifiers.