Omni-Dimensional Frequency Learner for General Time Series Analysis

• URL: http://arxiv.org/abs/2407.10419v2
• Date: Fri, 19 Jul 2024 03:00:16 GMT
• Title: Omni-Dimensional Frequency Learner for General Time Series Analysis
• Authors: Xianing Chen, Hanting Chen, Hailin Hu,
• Abstract summary: We present Omni-Dimensional Frequency Learner (ODFL) model based on a in depth analysis among all the three aspects of the spectrum feature. Technically, our method is composed of a semantic-adaptive global filter with attention to the un-salient frequency bands and partial operation among the channel dimension. ODFL achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.
• Score: 12.473862872616998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequency domain representation of time series feature offers a concise representation for handling real-world time series data with inherent complexity and dynamic nature. However, current frequency-based methods with complex operations still fall short of state-of-the-art time domain methods for general time series analysis. In this work, we present Omni-Dimensional Frequency Learner (ODFL) model based on a in depth analysis among all the three aspects of the spectrum feature: channel redundancy property among the frequency dimension, the sparse and un-salient frequency energy distribution among the frequency dimension, and the semantic diversity among the variable dimension. Technically, our method is composed of a semantic-adaptive global filter with attention to the un-salient frequency bands and partial operation among the channel dimension. Empirical results show that ODFL achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection, offering a promising foundation for time series analysis.

Related papers

• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts [103.725112190618]
  This paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
  arXiv  Detail & Related papers  (2024-10-14T13:01:11Z)
• FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting [13.253624747448935]
  Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. Current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. We propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components.
  arXiv  Detail & Related papers  (2024-05-22T02:37:02Z)
• ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting [7.694820760102176]
  ATFNet is an innovative framework that combines a time domain module and a frequency domain module. We introduce Dominant Harmonic Series Energy Weighting, a novel mechanism for adjusting the weights between the two modules. Our Complex-valued Spectrum Attention mechanism offers a novel approach to discern the intricate relationships between different frequency combinations.
  arXiv  Detail & Related papers  (2024-04-08T04:41:39Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [67.60443290781988]
  Time series forecasting has played the key role in different industrial domains, including finance, traffic, energy, and healthcare. Most-based forecasting methods suffer from the point-wise mappings and information bottleneck. We propose FreTS, a simple yet effective architecture built upon Frequency-domains for Time Series forecasting.
  arXiv  Detail & Related papers  (2023-11-10T17:05:13Z)
• A Survey on Deep Learning based Time Series Analysis with Frequency Transformation [74.3919960186696]
  Frequency transformation (FT) has been increasingly incorporated into deep learning models to enhance state-of-the-art accuracy and efficiency in time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. We present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT.
  arXiv  Detail & Related papers  (2023-02-04T14:33:07Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis [12.867257563413972]
  Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. We propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD, to exploit both time and frequency domains for performance improvement.
  arXiv  Detail & Related papers  (2022-10-18T09:08:57Z)
• Deep Autoregressive Models with Spectral Attention [74.08846528440024]
  We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
  arXiv  Detail & Related papers  (2021-07-13T11:08:47Z)
• Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data [2.36991223784587]
  We propose a data-driven approach to time-frequency analysis that circumvents many of the shortcomings of classic approaches. The method is demonstrated on a diversity of time-series data, including on data from cantilever-based electrostatic force microscopy.
  arXiv  Detail & Related papers  (2021-04-03T02:19:07Z)
• Bayesian Reconstruction of Fourier Pairs [21.104218472462907]
  General literature fails to cater for missing observations and noise-corrupted data. Our aim is to address the lack of a principled treatment of data acquired indistinctly in the temporal and frequency domains. We show that the proposed model is able to perform joint time and frequency reconstruction of real-world audio, healthcare and astronomy signals.
  arXiv  Detail & Related papers  (2020-11-09T17:30:24Z)

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.