Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

• URL: http://arxiv.org/abs/2302.13563v2
• Date: Fri, 22 Sep 2023 14:21:07 GMT
• Title: Deep Imbalanced Time-series Forecasting via Local Discrepancy Density
• Authors: Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, Edward Choi
• Abstract summary: We propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. We demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.
• Score: 45.17779967536991
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.

Related papers

• Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification [10.816479922364097]
  We propose a general Amortized Predictability-aware Training Framework (APTF) for both time series forecasting (TSF) and time series classification (TSC)<n>APTF introduces two key designs that enable the model to focus on high-predictability samples while still learning appropriately from low-predictability ones.
  arXiv  Detail & Related papers  (2026-02-18T06:59:05Z)
• An Adversarial Learning Approach to Irregular Time-Series Forecasting [0.032771631221674334]
  We propose an adversarial learning framework with a deep analysis of adversarial components to better capture the nuances of irregular time series. Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.
  arXiv  Detail & Related papers  (2024-11-28T19:28:07Z)
• Training and Evaluating Causal Forecasting Models for Time-Series [1.1218431616419589]
  We extend the statistical learning framework to train causal time-series models that generalize better when forecasting the effect of actions outside of their training distribution. We leverage Regression Discontinuity Designs popular in economics to construct a test set of causal treatment effects.
  arXiv  Detail & Related papers  (2024-10-31T18:27:54Z)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [53.27596811146316]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls [77.42510898755037]
  One More Step (OMS) is a compact network that incorporates an additional simple yet effective step during inference. OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters. Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.
  arXiv  Detail & Related papers  (2023-11-27T12:02:42Z)
• WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting [30.692056599222926]
  Time series forecasting has become a critical task due to its high practicality in real-world applications. Recent deep-learning-based approaches have shown remarkable success in time series forecasting. Deep networks still suffer from unstable training and overfitting.
  arXiv  Detail & Related papers  (2022-10-25T19:58:02Z)
• Time Series Forecasting Models Copy the Past: How to Mitigate [24.397660153755997]
  In the presence of noise and uncertainty, neural network models tend to replicate the last observed value of the time series. We propose a regularization term penalizing the replication of previously seen values. Our results indicate that the regularization term mitigates to some extent the aforementioned problem and gives rise to more robust models.
  arXiv  Detail & Related papers  (2022-07-27T10:39:00Z)
• Meta-Forecasting by combining Global DeepRepresentations with Local Adaptation [12.747008878068314]
  We introduce a novel forecasting method called Meta Global-Local Auto-Regression (Meta-GLAR) It adapts to each time series by learning in closed-form the mapping from the representations produced by a recurrent neural network (RNN) to one-step-ahead forecasts. Our method is competitive with the state-of-the-art in out-of-sample forecasting accuracy reported in earlier work.
  arXiv  Detail & Related papers  (2021-11-05T11:45:02Z)
• On the Generalization of Stochastic Gradient Descent with Momentum [58.900860437254885]
  We first show that there exists a convex loss function for which algorithmic stability fails to establish generalization guarantees. For smooth Lipschitz loss functions, we analyze a modified momentum-based update rule, and show that it admits an upper-bound on the generalization error. For the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes.
  arXiv  Detail & Related papers  (2021-02-26T18:58:29Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• On the Generalization of Stochastic Gradient Descent with Momentum [84.54924994010703]
  momentum-based accelerated variants of gradient descent (SGD) are widely used when training machine learning models. We first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. For smooth Lipschitz loss functions, we analyze a modified momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes.
  arXiv  Detail & Related papers  (2018-09-12T17:02:08Z)

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.