Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in FutureDownload PDF

29 Sept 2021, 00:36 (edited 15 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: Epidemic Forecasting, Data revisions, Graph Representation learning, Time Series Forecasting
  • Abstract: For real-time forecasting in domains like public health and macroeconomics, data collection is a non-trivial and demanding task. Often after being initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches a stable value. This so-called ‘backfill’ phenomenon and its effect on model performance have been barely addressed in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework, Back2Future, that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of the diverse set of top models for COVID-19 forecasting and GDP growth forecasting. Specifically, we show that Back2Future refined top COVID-19 models by 6.65% to 11.24% and yield an 18% improvement over non-trivial baselines. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.
  • One-sentence Summary: We study the problem of multi-variate backfill for both features and targets and show how to leverage our insights for more general neural framework to improve both model predictions and evaluation
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