Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
Accurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Auto...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3768 |
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| author | Xiangkai Ma Huaxiong Zhang |
| author_facet | Xiangkai Ma Huaxiong Zhang |
| author_sort | Xiangkai Ma |
| collection | DOAJ |
| description | Accurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Autoformer model. Based on multi-scale convolutional operations, a multi-scale feature fusion network is proposed, combined with date–time encoding to build the MD–Autoformer time series forecasting model, which enhances the model’s ability to capture information at different scales. In forecasting tasks across four fields—apparel sales, meteorology, finance, and disease—the proposed method achieved the lowest RMSE and MAE. Additionally, ablation experiments demonstrated the effectiveness and reliability of the proposed method. Combined with the TPE Bayesian optimization algorithm, the prediction error was further reduced, providing a reference for future research on time series forecasting methods. |
| format | Article |
| id | doaj-art-0f93c0018a384645869e71e28e42a04c |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0f93c0018a384645869e71e28e42a04c2025-08-20T03:06:32ZengMDPI AGApplied Sciences2076-34172025-03-01157376810.3390/app15073768Time Series Forecasting Method Based on Multi-Scale Feature Fusion and AutoformerXiangkai Ma0Huaxiong Zhang1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310000, ChinaSchool of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310000, ChinaAccurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Autoformer model. Based on multi-scale convolutional operations, a multi-scale feature fusion network is proposed, combined with date–time encoding to build the MD–Autoformer time series forecasting model, which enhances the model’s ability to capture information at different scales. In forecasting tasks across four fields—apparel sales, meteorology, finance, and disease—the proposed method achieved the lowest RMSE and MAE. Additionally, ablation experiments demonstrated the effectiveness and reliability of the proposed method. Combined with the TPE Bayesian optimization algorithm, the prediction error was further reduced, providing a reference for future research on time series forecasting methods.https://www.mdpi.com/2076-3417/15/7/3768time series forecastingmulti-scale feature fusion networkdate–time encodingAutoformerBayesian optimization algorithm |
| spellingShingle | Xiangkai Ma Huaxiong Zhang Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer Applied Sciences time series forecasting multi-scale feature fusion network date–time encoding Autoformer Bayesian optimization algorithm |
| title | Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer |
| title_full | Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer |
| title_fullStr | Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer |
| title_full_unstemmed | Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer |
| title_short | Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer |
| title_sort | time series forecasting method based on multi scale feature fusion and autoformer |
| topic | time series forecasting multi-scale feature fusion network date–time encoding Autoformer Bayesian optimization algorithm |
| url | https://www.mdpi.com/2076-3417/15/7/3768 |
| work_keys_str_mv | AT xiangkaima timeseriesforecastingmethodbasedonmultiscalefeaturefusionandautoformer AT huaxiongzhang timeseriesforecastingmethodbasedonmultiscalefeaturefusionandautoformer |