# POLA: Online Time Series Prediction by Adaptive Learning Rates

arXiv: https://arxiv.org/abs/2102.08907 · HTML: https://mlsanity.com/paper/2102.08907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Deep Bilevel Learning](https://mlsanity.com/paper/1809.01465/) (2018)
- [Online Learning for Time Series Prediction](https://mlsanity.com/paper/1302.6927/) (2013)
- [Proximal Algorithms in Statistics and Machine Learning](https://mlsanity.com/paper/1502.03175/) (2015)
- [Multi-label Prediction in Time Series Data using Deep Neural Networks](https://mlsanity.com/paper/2001.10098/) (2020)
- [Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction](https://mlsanity.com/paper/1811.00620/) (2018)
- [ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation](https://mlsanity.com/paper/1810.06167/) (2018)

## Built on by (most cited first, 1 of 1)

- [Lyapunov-Stable Adaptive Control for Multimodal Concept Drift](https://mlsanity.com/paper/2510.15944/) (2025)

## Abstract (arXiv)

Online prediction for streaming time series data has practical use for many real-world applications where downstream decisions depend on accurate forecasts for the future. Deployment in dynamic environments requires models to adapt quickly to changing data distributions without overfitting. We propose POLA (Predicting Online by Learning rate Adaptation) to automatically regulate the learning rate of recurrent neural network models to adapt to changing time series patterns across time. POLA meta-learns the learning rate of the stochastic gradient descent (SGD) algorithm by assimilating the prequential or interleaved-test-then-train evaluation scheme for online prediction. We evaluate POLA on two real-world datasets across three commonly-used recurrent neural network models. POLA demonstrates overall comparable or better predictive performance over other online prediction methods.