SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters Article Swipe

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Shengsheng Lin , Weiwei Lin , Wentai Wu , Haojun Chen , Junjie Yang ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2405.00946

This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF.

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Term (time) Series (stratigraphy) Econometrics Time series Computer science Economics Machine learning Geology Physics Paleontology Quantum mechanics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2405.00946
PDF: https://arxiv.org/pdf/2405.00946
OA Status: green
Cited By: 8
Related Works: 10
OpenAlex ID: https://openalex.org/W4396650796

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DOI: https://doi.org/10.48550/arxiv.2405.00946

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Title: SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters

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Language: en

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Publication year: 2024

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Publication date: 2024-05-02

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Authors: Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang

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Landing page: https://arxiv.org/abs/2405.00946

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Concepts: Term (time), Series (stratigraphy), Econometrics, Time series, Computer science, Economics, Machine learning, Geology, Physics, Paleontology, Quantum mechanics

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Cited by: 8

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