Improving Event Duration Prediction via Time-aware Pre-training

• URL: http://arxiv.org/abs/2011.02610v1
• Date: Thu, 5 Nov 2020 01:52:11 GMT
• Title: Improving Event Duration Prediction via Time-aware Pre-training
• Authors: Zonglin Yang, Xinya Du, Alexander Rush, Claire Cardie
• Abstract summary: We introduce two effective models for duration prediction. One model predicts the range/unit where the duration value falls in (R-pred); and the other predicts the exact duration value E-pred. Our best model -- E-pred, substantially outperforms previous work, and captures duration information more accurately than R-pred.
• Score: 90.74988936678723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training). Specifically, one model predicts the range/unit where the duration value falls in (R-pred); and the other predicts the exact duration value E-pred. Our best model -- E-pred, substantially outperforms previous work, and captures duration information more accurately than R-pred. We also demonstrate our models are capable of duration prediction in the unsupervised setting, outperforming the baselines.

Related papers

• Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data [6.296946118570559]
  This paper explores the application of a pre-trained large time series model, Timer, in the prediction of SCADA data collected from wind turbines. The model was fine-tuned on datasets sourced from two wind farms, which exhibited differing characteristics.
  arXiv  Detail & Related papers  (2024-11-30T09:00:24Z)
• Future-Guided Learning: A Predictive Approach To Enhance Time-Series Forecasting [4.866362841501992]
  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. 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 48.7% reduction in MSE for forecasting in nonlinear dynamical systems.
  arXiv  Detail & Related papers  (2024-10-19T21:22:55Z)
• Forecasting with Deep Learning: Beyond Average of Average of Average Performance [0.393259574660092]
  Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score. We propose a novel framework for evaluating models from multiple perspectives. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques.
  arXiv  Detail & Related papers  (2024-06-24T12:28:22Z)
• STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning [11.324029387605888]
  We propose an early-temporal forecasting model based on Multi-Objective reinforcement learning. Our method demonstrates superior performance on three large-scale real-world datasets.
  arXiv  Detail & Related papers  (2024-06-06T13:03:51Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models [11.171712535005357]
  We propose Deep Kernel Accelerated Failure Time models for the time-to-event prediction task. Our model shows better point estimate performance than recurrent neural network based baselines in experiments on two real-world datasets.
  arXiv  Detail & Related papers  (2021-07-26T14:55:02Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.