Fractional Fourier Transform in Time Series Prediction

Koc, Emirhan; Koç, Aykut

doi:10.1109/lsp.2022.3228131

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…Multimodal Rumor Detection time series (Cao et al 2020;Lange, Brunton, and Kutz 2021;Koc ¸and Koc ¸2022;Yang and Hong 2022). To increase the accuracy of multivariate time-series forecasting, Cao et al (Cao et al 2020) propose a spectral temporal graph neural network (StemGNN), which mines the correlations and time dependencies between sequences in the spectral domain.…”

Section: Related Workmentioning

confidence: 99%

Frequency Spectrum Is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector

Lao,

Zhang,

Shi

et al. 2024

AAAI

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Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modalities. However, they suffer from less discriminative unimodal representation and are vulnerable to intricate location dependencies in the time-consuming fusion of spatial and sequential tokens. This work makes the first attempt at multimodal rumor detection in the frequency domain, which efficiently transforms spatial features into the frequency spectrum and obtains highly discriminative spectrum features for multimodal representation and fusion. A novel Frequency Spectrum Representation and fUsion network (FSRU) with dual contrastive learning reveals the frequency spectrum is more effective for multimodal representation and fusion, extracting the informative components for rumor detection. FSRU involves three novel mechanisms: utilizing the Fourier transform to convert features in the spatial domain to the frequency domain, the unimodal spectrum compression, and the cross-modal spectrum co-selection module in the frequency domain. Substantial experiments show that FSRU achieves satisfactory multimodal rumor detection performance.

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Section: Related Workmentioning

confidence: 99%

Frequency Spectrum Is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector

Lao,

Zhang,

Shi

et al. 2024

AAAI

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“…For instance, the fusion of convolutional neural networks (CNNs) with recurrent neural networks (RNNs), as seen in , leverages the strengths of both architectures to enhance predictive performance. Similarly, the application of fractional Fourier transforms by Koç and Koç (2022) introduces a novel perspective on feature extraction, providing a fresh avenue for improving prediction accuracy. Echo State Networks (ESNs) have also garnered attention for their capacity to handle nonlinear and chaotic time series, with modifications such as chained multiple-subnetwork configurations and hierarchical strategies aimed at optimizing their structure and learning capabilities.…”

mentioning

confidence: 99%

Design of an Iterative Method for Enhanced Multimodal Time Series Analysis Using Graph Attention Networks, Variational Graph Autoencoders, and Transfer Learning

Vijaya Kamble

2024

jes

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In the ever-evolving landscape of data analysis, the need to efficiently and accurately interpret multimodal time series data has become paramount. Traditional methods often fall short in addressing the complex dependencies and dynamics inherent in such data, limiting their effectiveness in real-world applications. This work introduces a comprehensive approach that leverages Graph Attention Networks (GATs), Variational Graph Autoencoders (VGAEs), transfer learning with pretrained transformers, and Bayesian state-space models to overcome these limitations. GATs are selected for their ability to dynamically focus on relevant modalities through attention mechanisms, thereby capturing the intricate relationships between different data modalities. This method significantly enhances the model's ability to integrate multimodal information, leading to notable improvements in classification, prediction, and anomaly detection tasks. VGAEs are utilized to learn latent representations within a graph-based framework, promoting unsupervised learning while unveiling the underlying data structure. The resultant embeddings are pivotal for downstream tasks like clustering and visualization, encapsulating the interactions within multimodal time series data effectively. Furthermore, this work incorporates transfer learning with pretrained transformers to harness extensive knowledge from large datasets, adapting it to multimodal time series analysis. This strategy excels in capturing long-range dependencies, thereby augmenting generalization and performance in data-scarce scenarios. Bayesian state-space models are employed to elucidate the temporal dynamics and uncertainties of time series data, offering a robust framework for probabilistic inference and enhancing the interpretability and reliability of forecasting and anomaly detection. The efficacy of the proposed model is rigorously evaluated using diverse datasets, including the Yahoo! Stock Dataset, Forest Cover Dataset, and an empirical collection of 100k time series data samples. The results demonstrate a significant leap in performance metrics, including a 9.5% increase in precision, 8.5% boost in accuracy, 8.3% rise in recall, 10.4% reduction in delay, 9.4% enhancement in AUC, and a 5.9% improvement in specificity, alongside superior pre-emption capabilities compared to existing methods. This work not only addresses the pressing need for advanced multimodal time series analysis techniques but also sets a new benchmark for efficiency and accuracy. The integration of GATs, VGAEs, transfer learning with pretrained transformers, and Bayesian state-space models presents a formidable approach that significantly advances the field, offering profound impacts on a wide array of applications.

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STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional Network

Xing,

Wei

et al. 2023

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

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Fractional Fourier Transform in Time Series Prediction

Cited by 7 publications

References 30 publications

Frequency Spectrum Is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector

Frequency Spectrum Is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector

Design of an Iterative Method for Enhanced Multimodal Time Series Analysis Using Graph Attention Networks, Variational Graph Autoencoders, and Transfer Learning

STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional Network

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