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Abstract

This study presents a novel time series prediction model(VLinear), designed with linear computational complexity, demonstrating superior predictive performance compared to DLiner without increasing parameter count or computational demands. Our model introduces two key innovations: first, a Feature Pyramid Network (FPN) is employed to effectively capture time series data characteristics, bypassing the traditional decomposition into trend and seasonal components. Second, a multi-layer fusion structure is developed to integrate deep and shallow features seamlessly. Empirically, VLinear outperforms DLiner in 31 out of 32 test cases on eight open-source datasets, with an average reduction of 16.8% in mean squared error (MSE) and 11.8% in mean absolute error (MAE). Additionally, compared to the transformer-based PatchTST, VLinear achieves 10 best MSE and 15 best MAE results, using only 8% of PatchTST’s total computational load in the 32 test projects.

Keywords

Long term time series prediction FPN feature fusion MLP

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References

Zhao

Guo

, et al. Fuzzy hypergraph network for recommending top-k profitable stocks. Inf Sci (Ny) 2022; 613: 239–255.

Liu

, et al. A stock price prediction method based on meta-learning and variational mode decomposition. Knowl Based Syst 2022; 252: 109324.

Zhao

, et al. Mpr-net:multi-scale pattern reproduction guided universality time series interpretable forecasting, 2023.

Helfenstein

. Box-jenkins modelling in medical research. Statistical Methods in Medical Research, 1996; 5: 3.

Rangapuram

Seeger

Gasthaus

, et al. Deep state space models for time series forecasting. Neural Information Processing Systems 2018: 7796–7805.

Bai

Kolter

Koltun

. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018.

Tolstikhin

Houlsby

Kolesnikov

, et al. Mlp-mixer: An all-mlp architecture for vision. Adv Neural Inf Process Syst 2021; 34: 24261–24272.

Nie

Nguyen

Sinthong

, et al. A time series is worth 64 words: Long-term forecasting with transformers. In International Conference on Learning Representations, 2023.

Woo

Liu

Sahoo

, et al. Xponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381v, 2022.

10.

Wang

, et al. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Adv Neural Inf Process Syst 2021; 34: 22419–22430.

11.

Zhou

Wen

, et al. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning, pp.27268–27286. PMLR, 2022.

12.

Vijay

Jati

Nguyen

, et al. Tsmixer: Lightweight mlp-mixer model for multivariate time series forecasting. arXiv preprint arXiv:2306.09364, 2024.

13.

Zeng

Chen

Zhang

, et al. Are transformers effective for time series forecasting? In: Proceedings of the AAAI conference on artificial intelligence, 2023, volume 37, pp.11121–11128.

14.

Zhou

Zhang

Peng

, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, 2021, volume 35, pp.11106–11115.

15.

Jin

Xuan

, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Proceedings of the 33rd International Conference on Neural Information Processing Systems 2019: 5243–5253.

16.

Salinas

Flunkert

Gasthaus

, et al. Deepar: Probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 2020; 36: 1181–1191.

17.

Zhao

Chen

, et al. Lstm network: a deep learning approach for short-term traffic forecast. IET Intell Trans Syst 2017; 11: 68–75.

18.

Lin

Dollár

Girshick

, et al. Feature pyramid networks for object detection. 2016.

19.

Devlin

Chang

M-W

Lee

, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805, 2018.

20.

Madhusudhanan

Burchert

Duong-Trung

, et al. Yformer: U-net inspired transformer architecture for far horizon time series forecasting, 2022.

21.

Liu

Liao

, et al. Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International conference on learning representations, 2021.

22.

Ronneberger

Fischer

Brox

. U-net: convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted Intervention–MICCAI 2015: 18th international conference, 2015, pp.234–241.

VLinear: Enhanced linear complexity time series forecasting model

Abstract

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