An Adversarial Learning Approach to Irregular Time-Series Forecasting

• URL: http://arxiv.org/abs/2411.19341v1
• Date: Thu, 28 Nov 2024 19:28:07 GMT
• Title: An Adversarial Learning Approach to Irregular Time-Series Forecasting
• Authors: Heejeong Nam, Jihyun Kim, Jimin Yeom,
• Abstract summary: 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.
• Score: 0.032771631221674334
• License:
• Abstract: Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in forecasts that often misalign with human intuition. To tackle these challenges, we propose an adversarial learning framework with a deep analysis of adversarial components. Specifically, we emphasize the importance of balancing the modeling of global distribution (overall patterns) and transition dynamics (localized temporal changes) 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.

Related papers

• Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting [15.431513584239047]
  Time series forecasting is critical in numerous real-world applications. Traditional forecasting techniques struggle when data is scarce or not available at all. Recent advancements often leverage large-scale foundation models for such tasks.
  arXiv  Detail & Related papers  (2024-11-24T07:44:39Z)
• Deconfounding Time Series Forecasting [1.5967186772129907]
  Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current observations of variables to predict future outcomes. We propose an enhanced forecasting approach that incorporates representations of latent confounders derived from historical data.
  arXiv  Detail & Related papers  (2024-10-27T12:45:42Z)
• Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
  We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
  arXiv  Detail & Related papers  (2024-07-02T20:14:32Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• 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)
• MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting [13.790498420659636]
  Time series forecasting has received wide interest from existing research due to its broad applications inherent challenging. This paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable.
  arXiv  Detail & Related papers  (2023-07-13T13:16:01Z)
• 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)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Model-Attentive Ensemble Learning for Sequence Modeling [86.4785354333566]
  We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
  arXiv  Detail & Related papers  (2021-02-23T05:23:35Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

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.