Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting

• URL: http://arxiv.org/abs/2401.11929v4
• Date: Wed, 16 Oct 2024 12:20:35 GMT
• Title: Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
• Authors: Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor W. Tsang, Hui Xiong,
• Abstract summary: Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks.
• Score: 46.63798583414426
• License:
• Abstract: Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches. While longer sequences inherently offer richer information for enhanced predictive precision, prevailing studies often respond by escalating model complexity. These intricate models can inflate into millions of parameters, resulting in prohibitive parameter scales. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation while achieving uniformly superior and robust results across various datasets. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks, using over 99 \% fewer parameters than the majority of competing methods. Through this work, we aim to unleash the power of a restricted set of parameters by capitalizing on domain characteristics--a timely reminder that in the realm of LTSF, bigger is not invariably better.

Related papers

• Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting [8.458068118782519]
  Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. They are constrained by their inherent inability to effectively address long-range dependencies in time series data. We introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples.
  arXiv  Detail & Related papers  (2024-10-28T06:17:20Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting [17.132063819650355]
  We propose Multi Scale Dilated Convolution Network (MSDCN) to capture the period and trend characteristics of long time series. We design different convolution blocks with exponentially growing dilations and varying kernel sizes to sample time series data at different scales. To validate the effectiveness of the proposed approach, we conduct experiments on eight challenging long-term time series forecasting benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-09T02:11:01Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
  We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
  arXiv  Detail & Related papers  (2023-04-11T13:15:33Z)
• Discovering Predictable Latent Factors for Time Series Forecasting [39.08011991308137]
  We develop a novel framework for inferring the intrinsic latent factors implied by the observable time series. We introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting.
  arXiv  Detail & Related papers  (2023-03-18T14:37:37Z)
• Grouped self-attention mechanism for a memory-efficient Transformer [64.0125322353281]
  Real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time. Time-series data are generally recorded over a long period of observation with long sequences owing to their periodic characteristics and long-range dependencies over time. We propose two novel modules, Grouped Self-Attention (GSA) and Compressed Cross-Attention (CCA) Our proposed model efficiently exhibited reduced computational complexity and performance comparable to or better than existing methods.
  arXiv  Detail & Related papers  (2022-10-02T06:58:49Z)
• Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting [16.934920617960085]
  DSARF approximates high dimensional data by a product variables between time dependent weights and spatially dependent factors. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive factorization. Our experiments attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-10T20:15:59Z)
• 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.