POLA: Online Time Series Prediction by Adaptive Learning Rates
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.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionTime SeriesTime Series AnalysisTime Series PredictionSimilar Papers 제목 키워드 기반
Neural Controlled Differential Equations for Online Prediction Tasks
Neural controlled differential equations (Neural CDEs) are a continuous-time extension of recurrent neural networks (RNNs), achieving state-of-the-art (SOTA) performance at modelling functions of irregular time series. I…
Irregular Time SeriesPredictionTime SeriesTime Series AnalysisLearning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, p…
ManagementRepresentation LearningTime SeriesTime Series Forecasting+1Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven
Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, …
Time SeriesTime Series ForecastingAdaptive Conformal Predictions for Time Series
Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. Whi…
Conformal PredictionDecision MakingPrediction IntervalsTime Series+2A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or predict…
FormPrediction Intervalsquantile regressionregression+2