Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2405.06419v3
• Date: Tue, 24 Sep 2024 12:57:39 GMT
• Title: Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
• Authors: Tianxiang Zhan, Yuanpeng He, Yong Deng, Zhen Li, Wenjie Du, Qingsong Wen,
• Abstract summary: We propose a novel backbone architecture named Time Evidence Fusion Network (TEFN) from the perspective of information fusion. The proposed TEFN balances accuracy, efficiency, stability, and interpretability, making it a desirable solution for time series forecasting.
• Score: 22.550778677778112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency. Consequently, the exploration of model architectures remains a perennially trending topic in research. To address these challenges, we propose a novel backbone architecture named Time Evidence Fusion Network (TEFN) from the perspective of information fusion. Specifically, we introduce the Basic Probability Assignment (BPA) Module based on evidence theory to capture the uncertainty of multivariate time series data from both channel and time dimensions. Additionally, we develop a novel multi-source information fusion method to effectively integrate the two distinct dimensions from BPA output, leading to improved forecasting accuracy. Lastly, we conduct extensive experiments to demonstrate that TEFN achieves performance comparable to state-of-the-art methods while maintaining significantly lower complexity and reduced training time. Also, our experiments show that TEFN exhibits high robustness, with minimal error fluctuations during hyperparameter selection. Furthermore, due to the fact that BPA is derived from fuzzy theory, TEFN offers a high degree of interpretability. Therefore, the proposed TEFN balances accuracy, efficiency, stability, and interpretability, making it a desirable solution for time series forecasting.

Related papers

• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model [0.0]
  We introduce the Multi-Channel Data Fusion Network (MCDFN), a hybrid architecture that integrates CNN, Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) Our comparative benchmarking demonstrates that MCDFN outperforms seven other deep-learning models. This research advances demand forecasting methodologies and offers practical guidelines for integrating MCDFN into supply chain systems.
  arXiv  Detail & Related papers  (2024-05-24T14:30:00Z)
• 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)
• Cumulative Distribution Function based General Temporal Point Processes [49.758080415846884]
  CuFun model represents a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF) Our approach addresses several critical issues inherent in traditional TPP modeling. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction.
  arXiv  Detail & Related papers  (2024-02-01T07:21:30Z)
• State Sequences Prediction via Fourier Transform for Representation Learning [111.82376793413746]
  We propose State Sequences Prediction via Fourier Transform (SPF), a novel method for learning expressive representations efficiently. We theoretically analyze the existence of structural information in state sequences, which is closely related to policy performance and signal regularity. Experiments demonstrate that the proposed method outperforms several state-of-the-art algorithms in terms of both sample efficiency and performance.
  arXiv  Detail & Related papers  (2023-10-24T14:47:02Z)
• 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)
• A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers [2.572906392867547]
  Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. Several deep learning models have been proposed in the literature for STLF, reporting promising results.
  arXiv  Detail & Related papers  (2023-02-23T17:11:04Z)
• 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 Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

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.