# A Predictive Approach To Enhance Time-Series Forecasting

arXiv: https://arxiv.org/abs/2410.15217 · HTML: https://mlsanity.com/paper/2410.15217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 19 Oct 2024.

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

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [A Review on Deep Learning Techniques for Video Prediction](https://mlsanity.com/paper/2004.05214/) (2020)
- [Predictive Coding: a Theoretical and Experimental Review](https://mlsanity.com/paper/2107.12979/) (2021)
- [Temporal Knowledge Distillation for On-device Audio Classification](https://mlsanity.com/paper/2110.14131/) (2021)
- [Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation](https://mlsanity.com/paper/2308.03432/) (2023)
- [Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection](https://mlsanity.com/paper/1701.08968/) (2017)

## Built on by (most cited first, 3 of 3)

- [Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference](https://mlsanity.com/paper/2602.00775/) (2026)
- [NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence](https://mlsanity.com/paper/2604.18637/) (2026)
- [Forecast-Then-Optimize Deep Learning Methods](https://mlsanity.com/paper/2506.13036/) (2025)

## Abstract (arXiv)

Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution shifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data. When discrepancies occur between the forecasting and detection models, a more significant update is applied to the forecasting model, effectively minimizing surprise, allowing the forecasting model to dynamically adjust its parameters. We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 23.4% reduction in MSE for forecasting in nonlinear dynamical systems (outlier excluded).By incorporating a predictive feedback mechanism, Future-Guided Learning advances how deep learning is applied to time-series forecasting.