Fast and Slow Streams for Online Time Series Forecasting Without Information Leakage

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Fast and Slow Streams for Online Time Series Forecasting Without 
Information Leakage"

By

Miss Ying Yee Ava LAU


Abstract:

Current research in online time series forecasting suffers from information 
leakage: models predict and then evaluate on historical time steps that have 
been backpropagated for parameter updates. This setting also misaligns with 
the real-world conception of forecasting, which typically emphasizes looking 
ahead and anticipating future uncertainties. This paper redefines online time 
series forecasting to focus on predicting unknown future steps and evaluates 
performance solely based on these predictions. Following this new setting, 
challenges arise in leveraging incomplete pairs of ground truth and 
prediction for backpropagation, as well as generalizing accurate information 
without overfitting to noises from recent data streams. To address these 
challenges, we propose a novel dual-stream framework for online forecasting 
(DSOF): a slow stream that updates with complete data using experience 
replay, and a fast stream that adapts to recent data through temporal 
difference learning. This dual-stream approach updates a teacher-student 
model learned through a residual learning strategy, generating predictions in 
a coarse-to-fine manner. Extensive experiments demonstrate its improvement in 
forecasting performance in changing environments.


Date:                   Tuesday, 17 December 2024

Time:                   11:00am - 1:00pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Raymond WONG

Committee Members:      Prof. Dit-Yan YEUNG (Supervisor)
                        Prof. Nevin ZHANG