A Novel Framework for Significant Wave Height Prediction based on Adaptive Feature Extraction Time-Frequency Network

• URL: http://arxiv.org/abs/2505.06688v1
• Date: Sat, 10 May 2025 16:25:31 GMT
• Title: A Novel Framework for Significant Wave Height Prediction based on Adaptive Feature Extraction Time-Frequency Network
• Authors: Jianxin Zhang, Lianzi Jiang, Xinyu Han, Xiangrong Wang,
• Abstract summary: Adaptive Feature Extraction Time-Frequency Network (AFE-TFNet) is proposed to improve prediction accuracy and stability.<n>It is an encoder-decoder rolling framework with two stages: feature extraction and feature fusion.<n>Results show AFE-TFNet significantly outperforms benchmark methods in terms of prediction accuracy.
• Score: 5.7146098061920885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise forecasting of significant wave height (Hs) is essential for the development and utilization of wave energy. The challenges in predicting Hs arise from its non-linear and non-stationary characteristics. The combination of decomposition preprocessing and machine learning models have demonstrated significant effectiveness in Hs prediction by extracting data features. However, decomposing the unknown data in the test set can lead to data leakage issues. To simultaneously achieve data feature extraction and prevent data leakage, a novel Adaptive Feature Extraction Time-Frequency Network (AFE-TFNet) is proposed to improve prediction accuracy and stability. It is encoder-decoder rolling framework. The encoder consists of two stages: feature extraction and feature fusion. In the feature extraction stage, global and local frequency domain features are extracted by combining Wavelet Transform (WT) and Fourier Transform (FT), and multi-scale frequency analysis is performed using Inception blocks. In the feature fusion stage, time-domain and frequency-domain features are integrated through dominant harmonic sequence energy weighting (DHSEW). The decoder employed an advanced long short-term memory (LSTM) model. Hourly measured wind speed (Ws), dominant wave period (DPD), average wave period (APD) and Hs from three stations are used as the dataset, and the four metrics are employed to evaluate the forecasting performance. Results show that AFE-TFNet significantly outperforms benchmark methods in terms of prediction accuracy. Feature extraction can significantly improve the prediction accuracy. DHSEW has substantially increased the accuracy of medium-term to long-term forecasting. The prediction accuracy of AFE-TFNet does not demonstrate significant variability with changes of rolling time window size. Overall, AFE-TFNet shows strong potential for handling complex signal forecasting.

Related papers

• Beyond Time: Cross-Dimensional Frequency Supervision for Time Series Forecasting [27.031050170275307]
  We propose a purely frequency domain supervision approach named cross-dimensional frequency (X-Freq) loss.<n>X-Freq can improve the forecasting performance by an average of 3.3% on long-term forecasting datasets and 27.7% on short-term ones.
  arXiv  Detail & Related papers  (2025-05-16T09:17:15Z)
• EMForecaster: A Deep Learning Framework for Time Series Forecasting in Wireless Networks with Distribution-Free Uncertainty Quantification [11.88654374102813]
  We develop a deep learning (DL) time series forecasting framework referred to as textitEMForecaster.<n> EMForecaster achieves superior performance across diverse datasets, spanning both short-term and long-term prediction horizons.<n>In point forecasting tasks, EMForecaster substantially outperforms current state-of-the-art DL approaches, showing improvements of 53.97% over the Transformer architecture and 38.44% over the average of all baseline models.
  arXiv  Detail & Related papers  (2025-03-31T18:10:08Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting [9.511600544581425]
  PFformer is a position-free Transformer-based model designed for single-target MTS forecasting.<n> PFformer integrates two novel embedding strategies: Enhanced Feature-based Embedding (EFE) and Auto-Encoder-based Embedding (AEE)
  arXiv  Detail & Related papers  (2025-02-27T22:21:27Z)
• Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation [39.7344214193566]
  We introduce a pioneering test-time adaptation framework tailored for time series forecasting (TSF)<n>TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training.<n>The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters.
  arXiv  Detail & Related papers  (2025-01-09T04:59:15Z)
• Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization [74.3339999119713]
  We develop a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies.<n>Our method first scales and decomposes the input time series, then thresholds and quantizes the wavelet coefficients, and finally pre-trains an autoregressive model to forecast coefficients for the forecast horizon.
  arXiv  Detail & Related papers  (2024-12-06T18:22:59Z)
• Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting [0.0]
  This research introduces two augmentation approaches using the discrete wavelet transform (DWT) to adjust frequency elements while preserving temporal dependencies in time series data. To the best of our knowledge, this is the first study to conduct extensive experiments on multivariate time series using DWT.
  arXiv  Detail & Related papers  (2024-08-20T15:42:10Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process [76.98721879039559]
  We propose SMURF-THP, a score-based method for learning Transformer Hawkes process and quantifying prediction uncertainty. Specifically, SMURF-THP learns the score function of events' arrival time based on a score-matching objective. We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
  arXiv  Detail & Related papers  (2023-10-25T03:33:45Z)
• WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting [61.64303388738395]
  We propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. Tests on various time series datasets show WFTNet consistently outperforms other state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-20T13:44:18Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers [2.7998963147546148]
  We propose 3 components to integrate with commonly used data-driven weather prediction models. These components are 1) a deep spatial transformer added to latent space of U-NETs to preserve equivariance, 2) a data-assimilation algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and 3) a multi-time-step algorithm, improving the accuracy of forecasts at short intervals.
  arXiv  Detail & Related papers  (2021-03-16T23:15:00Z)

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.